Research paper

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Data_Files_Arff.zip

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Data_Files_Arff/bolts.arff

% Data from StatLib (ftp stat.cmu.edu/datasets) % % SUMMARY: % % Data from an experiment on the affects of machine adjustments on % the time to count bolts. Data appear as the STATS (Issue 10) Challenge. % % DATA: % % Submitted by W. Robert Stephenson, Iowa State University % email: [email protected] % % A manufacturer of automotive accessories provides hardware, e.g. nuts, % bolts, washers and screws, to fasten the accessory to the car or truck. % Hardware is counted and packaged automatically. Specifically, bolts % are dumped into a large metal dish. A plate that forms the bottom of % the dish rotates counterclockwise. This rotation forces bolts to the % outside of the dish and up along a narrow ledge. Due to the vibration % of the dish caused by the spinning bottom plate, some bolts fall off % the ledge and back into the dish. The ledge spirals up to a point % where the bolts are allowed to drop into a pan on a conveyor belt. % As a bolt drops, it passes by an electronic eye that counts it. When % the electronic counter reaches the preset number of bolts, the % rotation is stopped and the conveyor belt is moved forward. % % There are several adjustments on the machine that affect its operation. % These include; a speed setting that controls the speed of rotation % (SPEED1) of the plate at the bottom of the dish, a total number of % bolts (TOTAL) to be counted, a second speed setting (SPEED2) that is % used to change the speed of rotation (usually slowing it down) for the % last few bolts, the number of bolts to be counted at this second speed % (NUMBER2), and the sensitivity of the electronic eye (SENS). The % sensitivity setting is to insure that the correct number of bolts are % counted. Too few bolts packaged causes customer complaints. Too many % bolts packaged increases costs. For each run conducted in this % experiment the correct number of bolts was counted. From an % engineering standpoint if the correct number of bolts is counted, the % sensitivity should not affect the time to count bolts. The measured % response is the time (TIME), in seconds, it takes to count the desired % number of bolts. In order to put times on a equal footing the % response to be analyzed is the time to count 20 bolts (T20BOLT). % Below are the data for 40 combinations of settings. RUN is the order % in which the data were collected. % % Analyze the data. What adjustments have the greatest effect on the % time to count 20 bolts? How would you adjust the machine to get % the shortest time to count 20 bolts? Are there any unusual features % to the data? % % The data description and data may be freely used for non-commercial % purposes and can be freely distributed. Copyright remains with the % author and STATS Magazine. % @relation bolts @attribute RUN integer @attribute SPEED1 integer @attribute TOTAL integer @attribute SPEED2 integer @attribute NUMBER2 integer @attribute SENS integer @attribute TIME real @attribute T20BOLT real @data 25, 2, 10, 1.5, 0, 6, 5.70, 11.40 24, 2, 10, 1.5, 0, 10, 17.56, 35.12 30, 2, 10, 1.5, 2, 6, 11.28, 22.56 2, 2, 10, 1.5, 2, 10, 8.39, 16.78 40, 2, 10, 2.5, 0, 6, 16.67, 33.34 37, 2, 10, 2.5, 0, 10, 12.04, 24.08 16, 2, 10, 2.5, 2, 6, 9.22, 18.44 22, 2, 10, 2.5, 2, 10, 3.94, 7.88 33, 2, 30, 1.5, 0, 6, 27.02, 18.01 17, 2, 30, 1.5, 0, 10, 19.46, 12.97 28, 2, 30, 1.5, 2, 6, 18.54, 12.36 27, 2, 30, 1.5, 2, 10, 25.70, 17.13 14, 2, 30, 2.5, 0, 6, 19.02, 12.68 13, 2, 30, 2.5, 0, 10, 22.39, 14.93 4, 2, 30, 2.5, 2, 6, 23.85, 15.90 21, 2, 30, 2.5, 2, 10, 30.12, 20.08 23, 6, 10, 1.5, 0, 6, 13.42, 26.84 35, 6, 10, 1.5, 0, 10, 34.26, 68.52 19, 6, 10, 1.5, 2, 6, 39.74, 79.48 34, 6, 10, 1.5, 2, 10, 10.60, 21.20 31, 6, 10, 2.5, 0, 6, 28.89, 57.78 9, 6, 10, 2.5, 0, 10, 35.61, 71.22 38, 6, 10, 2.5, 2, 6, 17.20, 34.40 15, 6, 10, 2.5, 2, 10, 6.00, 12.00 39, 6, 30, 1.5, 0, 6, 129.45, 86.30 8, 6, 30, 1.5, 0, 0, 107.38, 71.59 26, 6, 30, 1.5, 2, 6, 111.66, 74.44 11, 6, 30, 1.5, 2, 0, 109.10, 72.73 6, 6, 30, 2.5, 0, 6, 100.43, 66.95 20, 6, 30, 2.5, 0, 0, 109.28, 72.85 10, 6, 30, 2.5, 2, 6, 106.46, 70.97 32, 6, 30, 2.5, 2, 0, 134.01, 89.34 1, 4, 20, 2.0, 1, 8, 10.78, 10.78 3, 4, 20, 2.0, 1, 8, 9.39, 9.39 5, 4, 20, 2.0, 1, 8, 9.84, 9.84 7, 4, 20, 2.0, 1, 8, 13.94, 13.94 12, 4, 20, 2.0, 1, 8, 12.33, 12.33 18, 4, 20, 2.0, 1, 8, 7.32, 7.32 29, 4, 20, 2.0, 1, 8, 7.91, 7.91 36, 4, 20, 2.0, 1, 8, 15.58, 15.58

Data_Files_Arff/cpu.arff

% % As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction % using instance-based learning with encoding length selection. In Progress % in Connectionist-Based Information Systems. Singapore: Springer-Verlag. % @RELATION cpu @attribute vendor { adviser, amdahl, apollo, basf, bti, burroughs, c.r.d, cdc, cambex, dec, dg, formation, four-phase, gould, hp, harris, honeywell, ibm, ipl, magnuson, microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, sratus, wang} @attribute MYCT real @attribute MMIN real @attribute MMAX real @attribute CACH real @attribute CHMIN real @attribute CHMAX real @attribute class real @data adviser,125,256,6000,256,16,128,199 amdahl,29,8000,32000,32,8,32,253 amdahl,29,8000,32000,32,8,32,253 amdahl,29,8000,32000,32,8,32,253 amdahl,29,8000,16000,32,8,16,132 amdahl,26,8000,32000,64,8,32,290 amdahl,23,16000,32000,64,16,32,381 amdahl,23,16000,32000,64,16,32,381 amdahl,23,16000,64000,64,16,32,749 amdahl,23,32000,64000,128,32,64,1238 apollo,400,1000,3000,0,1,2,23 apollo,400,512,3500,4,1,6,24 basf,60,2000,8000,65,1,8,70 basf,50,4000,16000,65,1,8,117 bti,350,64,64,0,1,4,15 bti,200,512,16000,0,4,32,64 burroughs,167,524,2000,8,4,15,23 burroughs,143,512,5000,0,7,32,29 burroughs,143,1000,2000,0,5,16,22 burroughs,110,5000,5000,142,8,64,124 burroughs,143,1500,6300,0,5,32,35 burroughs,143,3100,6200,0,5,20,39 burroughs,143,2300,6200,0,6,64,40 burroughs,110,3100,6200,0,6,64,45 c.r.d,320,128,6000,0,1,12,28 c.r.d,320,512,2000,4,1,3,21 c.r.d,320,256,6000,0,1,6,28 c.r.d,320,256,3000,4,1,3,22 c.r.d,320,512,5000,4,1,5,28 c.r.d,320,256,5000,4,1,6,27 cdc,25,1310,2620,131,12,24,102 cdc,25,1310,2620,131,12,24,102 cdc,50,2620,10480,30,12,24,74 cdc,50,2620,10480,30,12,24,74 cdc,56,5240,20970,30,12,24,138 cdc,64,5240,20970,30,12,24,136 cdc,50,500,2000,8,1,4,23 cdc,50,1000,4000,8,1,5,29 cdc,50,2000,8000,8,1,5,44 cambex,50,1000,4000,8,3,5,30 cambex,50,1000,8000,8,3,5,41 cambex,50,2000,16000,8,3,5,74 cambex,50,2000,16000,8,3,6,74 cambex,50,2000,16000,8,3,6,74 dec,133,1000,12000,9,3,12,54 dec,133,1000,8000,9,3,12,41 dec,810,512,512,8,1,1,18 dec,810,1000,5000,0,1,1,28 dec,320,512,8000,4,1,5,36 dec,200,512,8000,8,1,8,38 dg,700,384,8000,0,1,1,34 dg,700,256,2000,0,1,1,19 dg,140,1000,16000,16,1,3,72 dg,200,1000,8000,0,1,2,36 dg,110,1000,4000,16,1,2,30 dg,110,1000,12000,16,1,2,56 dg,220,1000,8000,16,1,2,42 formation,800,256,8000,0,1,4,34 formation,800,256,8000,0,1,4,34 formation,800,256,8000,0,1,4,34 formation,800,256,8000,0,1,4,34 formation,800,256,8000,0,1,4,34 four-phase,125,512,1000,0,8,20,19 gould,75,2000,8000,64,1,38,75 gould,75,2000,16000,64,1,38,113 gould,75,2000,16000,128,1,38,157 hp,90,256,1000,0,3,10,18 hp,105,256,2000,0,3,10,20 hp,105,1000,4000,0,3,24,28 hp,105,2000,4000,8,3,19,33 hp,75,2000,8000,8,3,24,47 hp,75,3000,8000,8,3,48,54 hp,175,256,2000,0,3,24,20 harris,300,768,3000,0,6,24,23 harris,300,768,3000,6,6,24,25 harris,300,768,12000,6,6,24,52 harris,300,768,4500,0,1,24,27 harris,300,384,12000,6,1,24,50 harris,300,192,768,6,6,24,18 harris,180,768,12000,6,1,31,53 honeywell,330,1000,3000,0,2,4,23 honeywell,300,1000,4000,8,3,64,30 honeywell,300,1000,16000,8,2,112,73 honeywell,330,1000,2000,0,1,2,20 honeywell,330,1000,4000,0,3,6,25 honeywell,140,2000,4000,0,3,6,28 honeywell,140,2000,4000,0,4,8,29 honeywell,140,2000,4000,8,1,20,32 honeywell,140,2000,32000,32,1,20,175 honeywell,140,2000,8000,32,1,54,57 honeywell,140,2000,32000,32,1,54,181 honeywell,140,2000,32000,32,1,54,181 honeywell,140,2000,4000,8,1,20,32 ibm,57,4000,16000,1,6,12,82 ibm,57,4000,24000,64,12,16,171 ibm,26,16000,32000,64,16,24,361 ibm,26,16000,32000,64,8,24,350 ibm,26,8000,32000,0,8,24,220 ibm,26,8000,16000,0,8,16,113 ibm,480,96,512,0,1,1,15 ibm,203,1000,2000,0,1,5,21 ibm,115,512,6000,16,1,6,35 ibm,1100,512,1500,0,1,1,18 ibm,1100,768,2000,0,1,1,20 ibm,600,768,2000,0,1,1,20 ibm,400,2000,4000,0,1,1,28 ibm,400,4000,8000,0,1,1,45 ibm,900,1000,1000,0,1,2,18 ibm,900,512,1000,0,1,2,17 ibm,900,1000,4000,4,1,2,26 ibm,900,1000,4000,8,1,2,28 ibm,900,2000,4000,0,3,6,28 ibm,225,2000,4000,8,3,6,31 ibm,225,2000,4000,8,3,6,31 ibm,180,2000,8000,8,1,6,42 ibm,185,2000,16000,16,1,6,76 ibm,180,2000,16000,16,1,6,76 ibm,225,1000,4000,2,3,6,26 ibm,25,2000,12000,8,1,4,59 ibm,25,2000,12000,16,3,5,65 ibm,17,4000,16000,8,6,12,101 ibm,17,4000,16000,32,6,12,116 ibm,1500,768,1000,0,0,0,18 ibm,1500,768,2000,0,0,0,20 ibm,800,768,2000,0,0,0,20 ipl,50,2000,4000,0,3,6,30 ipl,50,2000,8000,8,3,6,44 ipl,50,2000,8000,8,1,6,44 ipl,50,2000,16000,24,1,6,82 ipl,50,2000,16000,24,1,6,82 ipl,50,8000,16000,48,1,10,128 magnuson,100,1000,8000,0,2,6,37 magnuson,100,1000,8000,24,2,6,46 magnuson,100,1000,8000,24,3,6,46 magnuson,50,2000,16000,12,3,16,80 magnuson,50,2000,16000,24,6,16,88 magnuson,50,2000,16000,24,6,16,88 microdata,150,512,4000,0,8,128,33 nas,115,2000,8000,16,1,3,46 nas,115,2000,4000,2,1,5,29 nas,92,2000,8000,32,1,6,53 nas,92,2000,8000,32,1,6,53 nas,92,2000,8000,4,1,6,41 nas,75,4000,16000,16,1,6,86 nas,60,4000,16000,32,1,6,95 nas,60,2000,16000,64,5,8,107 nas,60,4000,16000,64,5,8,117 nas,50,4000,16000,64,5,10,119 nas,72,4000,16000,64,8,16,120 nas,72,2000,8000,16,6,8,48 nas,40,8000,16000,32,8,16,126 nas,40,8000,32000,64,8,24,266 nas,35,8000,32000,64,8,24,270 nas,38,16000,32000,128,16,32,426 nas,48,4000,24000,32,8,24,151 nas,38,8000,32000,64,8,24,267 nas,30,16000,32000,256,16,24,603 ncr,112,1000,1000,0,1,4,19 ncr,84,1000,2000,0,1,6,21 ncr,56,1000,4000,0,1,6,26 ncr,56,2000,6000,0,1,8,35 ncr,56,2000,8000,0,1,8,41 ncr,56,4000,8000,0,1,8,47 ncr,56,4000,12000,0,1,8,62 ncr,56,4000,16000,0,1,8,78 ncr,38,4000,8000,32,16,32,80 ncr,38,4000,8000,32,16,32,80 ncr,38,8000,16000,64,4,8,142 ncr,38,8000,24000,160,4,8,281 ncr,38,4000,16000,128,16,32,190 nixdorf,200,1000,2000,0,1,2,21 nixdorf,200,1000,4000,0,1,4,25 nixdorf,200,2000,8000,64,1,5,67 perkin-elmer,250,512,4000,0,1,7,24 perkin-elmer,250,512,4000,0,4,7,24 perkin-elmer,250,1000,16000,1,1,8,64 prime,160,512,4000,2,1,5,25 prime,160,512,2000,2,3,8,20 prime,160,1000,4000,8,1,14,29 prime,160,1000,8000,16,1,14,43 prime,160,2000,8000,32,1,13,53 siemens,240,512,1000,8,1,3,19 siemens,240,512,2000,8,1,5,22 siemens,105,2000,4000,8,3,8,31 siemens,105,2000,6000,16,6,16,41 siemens,105,2000,8000,16,4,14,47 siemens,52,4000,16000,32,4,12,99 siemens,70,4000,12000,8,6,8,67 siemens,59,4000,12000,32,6,12,81 siemens,59,8000,16000,64,12,24,149 siemens,26,8000,24000,32,8,16,183 siemens,26,8000,32000,64,12,16,275 siemens,26,8000,32000,128,24,32,382 sperry,116,2000,8000,32,5,28,56 sperry,50,2000,32000,24,6,26,182 sperry,50,2000,32000,48,26,52,227 sperry,50,2000,32000,112,52,104,341 sperry,50,4000,32000,112,52,104,360 sperry,30,8000,64000,96,12,176,919 sperry,30,8000,64000,128,12,176,978 sperry,180,262,4000,0,1,3,24 sperry,180,512,4000,0,1,3,24 sperry,180,262,4000,0,1,3,24 sperry,180,512,4000,0,1,3,24 sperry,124,1000,8000,0,1,8,37 sperry,98,1000,8000,32,2,8,50 sratus,125,2000,8000,0,2,14,41 wang,480,512,8000,32,0,0,47 wang,480,1000,4000,0,0,0,25

Data_Files_Arff/credit.arff

@RELATION credit
@ATTRIBUTE A1 {b	a}
@ATTRIBUTE A2 REAL
@ATTRIBUTE A3 REAL
@ATTRIBUTE A4 {u	y	l	t}
@ATTRIBUTE A5 {g	p	gg}
@ATTRIBUTE A6 {c	d	cc	i	j	k	m	r	q	w	x	e	aa	ff}
@ATTRIBUTE A7 {v	h	bb	j	n	z	dd	ff	o}
@ATTRIBUTE A8 REAL
@ATTRIBUTE A9 {t	f}
@ATTRIBUTE A10 {t	f}
@ATTRIBUTE A11 REAL
@ATTRIBUTE A12 {t	f}
@ATTRIBUTE A13 {g	p	s}
@ATTRIBUTE A14 REAL
@ATTRIBUTE A15 REAL
@ATTRIBUTE class {+	-}
@DATA
b	30.83	0	u	g	w	v	1.25	t	t	01	f	g	00202	0	+
a	58.67	4.46	u	g	q	h	3.04	t	t	06	f	g	00043	560	+
a	24.50	0.5	u	g	q	h	1.5	t	f	0	f	g	00280	824	+
b	27.83	1.54	u	g	w	v	3.75	t	t	05	t	g	00100	3	+
b	20.17	5.625	u	g	w	v	1.71	t	f	0	f	s	00120	0	+
b	32.08	4	u	g	m	v	2.5	t	f	0	t	g	00360	0	+
b	33.17	1.04	u	g	r	h	6.5	t	f	0	t	g	00164	31285	+
a	22.92	11.585	u	g	cc	v	0.04	t	f	0	f	g	00080	1349	+
b	54.42	0.5	y	p	k	h	3.96	t	f	0	f	g	00180	314	+
b	42.50	4.915	y	p	w	v	3.165	t	f	0	t	g	00052	1442	+
b	22.08	0.83	u	g	c	h	2.165	f	f	0	t	g	00128	0	+
b	29.92	1.835	u	g	c	h	4.335	t	f	0	f	g	00260	200	+
a	38.25	6	u	g	k	v	1	t	f	0	t	g	00000	0	+
b	48.08	6.04	u	g	k	v	0.04	f	f	0	f	g	00000	2690	+
a	45.83	10.5	u	g	q	v	5	t	t	07	t	g	00000	0	+
b	36.67	4.415	y	p	k	v	0.25	t	t	10	t	g	00320	0	+
b	28.25	0.875	u	g	m	v	0.96	t	t	03	t	g	00396	0	+
a	23.25	5.875	u	g	q	v	3.17	t	t	10	f	g	00120	245	+
b	21.83	0.25	u	g	d	h	0.665	t	f	0	t	g	00000	0	+
a	19.17	8.585	u	g	cc	h	0.75	t	t	07	f	g	00096	0	+
b	25.00	11.25	u	g	c	v	2.5	t	t	17	f	g	00200	1208	+
b	23.25	1	u	g	c	v	0.835	t	f	0	f	s	00300	0	+
a	47.75	8	u	g	c	v	7.875	t	t	06	t	g	00000	1260	+
a	27.42	14.5	u	g	x	h	3.085	t	t	01	f	g	00120	11	+
a	41.17	6.5	u	g	q	v	0.5	t	t	03	t	g	00145	0	+
a	15.83	0.585	u	g	c	h	1.5	t	t	02	f	g	00100	0	+
a	47.00	13	u	g	i	bb	5.165	t	t	09	t	g	00000	0	+
b	56.58	18.5	u	g	d	bb	15	t	t	17	t	g	00000	0	+
b	57.42	8.5	u	g	e	h	7	t	t	03	f	g	00000	0	+
b	42.08	1.04	u	g	w	v	5	t	t	06	t	g	00500	10000	+
b	29.25	14.79	u	g	aa	v	5.04	t	t	05	t	g	00168	0	+
b	42.00	9.79	u	g	x	h	7.96	t	t	08	f	g	00000	0	+
b	49.50	7.585	u	g	i	bb	7.585	t	t	15	t	g	00000	5000	+
a	36.75	5.125	u	g	e	v	5	t	f	0	t	g	00000	4000	+
a	22.58	10.75	u	g	q	v	0.415	t	t	05	t	g	00000	560	+
b	27.83	1.5	u	g	w	v	2	t	t	11	t	g	00434	35	+
b	27.25	1.585	u	g	cc	h	1.835	t	t	12	t	g	00583	713	+
a	23.00	11.75	u	g	x	h	0.5	t	t	02	t	g	00300	551	+
b	27.75	0.585	y	p	cc	v	0.25	t	t	02	f	g	00260	500	+
b	54.58	9.415	u	g	ff	ff	14.415	t	t	11	t	g	00030	300	+
b	34.17	9.17	u	g	c	v	4.5	t	t	12	t	g	00000	221	+
b	28.92	15	u	g	c	h	5.335	t	t	11	f	g	00000	2283	+
b	29.67	1.415	u	g	w	h	0.75	t	t	01	f	g	00240	100	+
b	39.58	13.915	u	g	w	v	8.625	t	t	06	t	g	00070	0	+
b	56.42	28	y	p	c	v	28.5	t	t	40	f	g	00000	15	+
b	54.33	6.75	u	g	c	h	2.625	t	t	11	t	g	00000	284	+
a	41.00	2.04	y	p	q	h	0.125	t	t	23	t	g	00455	1236	+
b	31.92	4.46	u	g	cc	h	6.04	t	t	03	f	g	00311	300	+
b	41.50	1.54	u	g	i	bb	3.5	f	f	0	f	g	00216	0	+
b	23.92	0.665	u	g	c	v	0.165	f	f	0	f	g	00100	0	+
a	25.75	0.5	u	g	c	h	0.875	t	f	0	t	g	00491	0	+
b	26.00	1	u	g	q	v	1.75	t	f	0	t	g	00280	0	+
b	37.42	2.04	u	g	w	v	0.04	t	f	0	t	g	00400	5800	+
b	34.92	2.5	u	g	w	v	0	t	f	0	t	g	00239	200	+
b	34.25	3	u	g	cc	h	7.415	t	f	0	t	g	00000	0	+
b	23.33	11.625	y	p	w	v	0.835	t	f	0	t	g	00160	300	+
b	23.17	0	u	g	cc	v	0.085	t	f	0	f	g	00000	0	+
b	44.33	0.5	u	g	i	h	5	t	f	0	t	g	00320	0	+
b	35.17	4.5	u	g	x	h	5.75	f	f	0	t	s	00711	0	+
b	43.25	3	u	g	q	h	6	t	t	11	f	g	00080	0	+
b	56.75	12.25	u	g	m	v	1.25	t	t	04	t	g	00200	0	+
b	31.67	16.165	u	g	d	v	3	t	t	09	f	g	00250	730	+
a	23.42	0.79	y	p	q	v	1.5	t	t	02	t	g	00080	400	+
a	20.42	0.835	u	g	q	v	1.585	t	t	01	f	g	00000	0	+
b	26.67	4.25	u	g	cc	v	4.29	t	t	01	t	g	00120	0	+
b	34.17	1.54	u	g	cc	v	1.54	t	t	01	t	g	00520	50000	+
a	36.00	1	u	g	c	v	2	t	t	11	f	g	00000	456	+
b	25.50	0.375	u	g	m	v	0.25	t	t	03	f	g	00260	15108	+
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a	19.17	5.415	u	g	i	h	0.29	f	f	0	f	g	00080	484	-
a	18.17	10	y	p	q	h	0.165	f	f	0	f	g	00340	0	-
b	24.58	13.5	y	p	ff	ff	0	f	f	0	f	g	?	0	-
b	16.25	0.835	u	g	m	v	0.085	t	f	0	f	s	00200	0	-
b	21.17	0.875	y	p	c	h	0.25	f	f	0	f	g	00280	204	-
b	23.92	0.585	y	p	cc	h	0.125	f	f	0	f	g	00240	1	-
b	17.67	4.46	u	g	c	v	0.25	f	f	0	f	s	00080	0	-
a	16.50	1.25	u	g	q	v	0.25	f	t	01	f	g	00108	98	-
b	23.25	12.625	u	g	c	v	0.125	f	t	02	f	g	00000	5552	-
b	17.58	10	u	g	w	h	0.165	f	t	01	f	g	00120	1	-
a	?	1.5	u	g	ff	ff	0	f	t	02	t	g	00200	105	-
b	29.50	0.58	u	g	w	v	0.29	f	t	01	f	g	00340	2803	-
b	18.83	0.415	y	p	c	v	0.165	f	t	01	f	g	00200	1	-
a	21.75	1.75	y	p	j	j	0	f	f	0	f	g	00160	0	-
b	23.00	0.75	u	g	m	v	0.5	f	f	0	t	s	00320	0	-
a	18.25	10	u	g	w	v	1	f	t	01	f	g	00120	1	-
b	25.42	0.54	u	g	w	v	0.165	f	t	01	f	g	00272	444	-
b	35.75	2.415	u	g	w	v	0.125	f	t	02	f	g	00220	1	-
a	16.08	0.335	u	g	ff	ff	0	f	t	01	f	g	00160	126	-
a	31.92	3.125	u	g	ff	ff	3.04	f	t	02	t	g	00200	4	-
b	69.17	9	u	g	ff	ff	4	f	t	01	f	g	00070	6	-
b	32.92	2.5	u	g	aa	v	1.75	f	t	02	t	g	00720	0	-
b	16.33	2.75	u	g	aa	v	0.665	f	t	01	f	g	00080	21	-
b	22.17	12.125	u	g	c	v	3.335	f	t	02	t	g	00180	173	-
a	57.58	2	u	g	ff	ff	6.5	f	t	01	f	g	00000	10	-
b	18.25	0.165	u	g	d	v	0.25	f	f	0	t	s	00280	0	-
b	23.42	1	u	g	c	v	0.5	f	f	0	t	s	00280	0	-
a	15.92	2.875	u	g	q	v	0.085	f	f	0	f	g	00120	0	-
a	24.75	13.665	u	g	q	h	1.5	f	f	0	f	g	00280	1	-
b	48.75	26.335	y	p	ff	ff	0	t	f	0	t	g	00000	0	-
b	23.50	2.75	u	g	ff	ff	4.5	f	f	0	f	g	00160	25	-
b	18.58	10.29	u	g	ff	ff	0.415	f	f	0	f	g	00080	0	-
b	27.75	1.29	u	g	k	h	0.25	f	f	0	t	s	00140	0	-
a	31.75	3	y	p	j	j	0	f	f	0	f	g	00160	20	-
a	24.83	4.5	u	g	w	v	1	f	f	0	t	g	00360	6	-
b	19.00	1.75	y	p	c	v	2.335	f	f	0	t	g	00112	6	-
a	16.33	0.21	u	g	aa	v	0.125	f	f	0	f	g	00200	1	-
a	18.58	10	u	g	d	v	0.415	f	f	0	f	g	00080	42	-
b	16.25	0	y	p	aa	v	0.25	f	f	0	f	g	00060	0	-
b	23.00	0.75	u	g	m	v	0.5	t	f	0	t	s	00320	0	-
b	21.17	0.25	y	p	c	h	0.25	f	f	0	f	g	00280	204	-
b	17.50	22	l	gg	ff	o	0	f	f	0	t	p	00450	100000	+
b	19.17	0	y	p	m	bb	0	f	f	0	t	s	00500	1	+
b	36.75	0.125	y	p	c	v	1.5	f	f	0	t	g	00232	113	+
b	21.25	1.5	u	g	w	v	1.5	f	f	0	f	g	00150	8	+
a	18.08	0.375	l	gg	cc	ff	10	f	f	0	t	s	00300	0	+
a	33.67	0.375	u	g	cc	v	0.375	f	f	0	f	g	00300	44	+
b	48.58	0.205	y	p	k	v	0.25	t	t	11	f	g	00380	2732	+
b	33.67	1.25	u	g	w	v	1.165	f	f	0	f	g	00120	0	-
a	29.50	1.085	y	p	x	v	1	f	f	0	f	g	00280	13	-
b	30.17	1.085	y	p	c	v	0.04	f	f	0	f	g	00170	179	-
?	40.83	3.5	u	g	i	bb	0.5	f	f	0	f	s	01160	0	-
b	34.83	2.5	y	p	w	v	3	f	f	0	f	s	00200	0	-
b	?	4	y	p	i	v	0.085	f	f	0	t	g	00411	0	-
b	20.42	0	?	?	?	?	0	f	f	0	f	p	?	0	-
a	33.25	2.5	y	p	c	v	2.5	f	f	0	t	g	00000	2	-
b	34.08	2.5	u	g	c	v	1	f	f	0	f	g	00460	16	-
a	25.25	12.5	u	g	d	v	1	f	f	0	t	g	00180	1062	-
b	34.75	2.5	u	g	cc	bb	0.5	f	f	0	f	g	00348	0	-
b	27.67	0.75	u	g	q	h	0.165	f	f	0	t	g	00220	251	-
b	47.33	6.5	u	g	c	v	1	f	f	0	t	g	00000	228	-
a	34.83	1.25	y	p	i	h	0.5	f	f	0	t	g	00160	0	-
a	33.25	3	y	p	aa	v	2	f	f	0	f	g	00180	0	-
b	28.00	3	u	g	w	v	0.75	f	f	0	t	g	00300	67	-
a	39.08	4	u	g	c	v	3	f	f	0	f	g	00480	0	-
b	42.75	4.085	u	g	aa	v	0.04	f	f	0	f	g	00108	100	-
b	26.92	2.25	u	g	i	bb	0.5	f	f	0	t	g	00640	4000	-
b	33.75	2.75	u	g	i	bb	0	f	f	0	f	g	00180	0	-
b	38.92	1.75	u	g	k	v	0.5	f	f	0	t	g	00300	2	-
b	62.75	7	u	g	e	z	0	f	f	0	f	g	00000	12	-
?	32.25	1.5	u	g	c	v	0.25	f	f	0	t	g	00372	122	-
b	26.75	4.5	y	p	c	bb	2.5	f	f	0	f	g	00200	1210	-
b	63.33	0.54	u	g	c	v	0.585	t	t	03	t	g	00180	0	-
b	27.83	1.5	u	g	w	v	2.25	f	t	01	t	g	00100	3	-
a	26.17	2	u	g	j	j	0	f	f	0	t	g	00276	1	-
b	22.17	0.585	y	p	ff	ff	0	f	f	0	f	g	00100	0	-
b	22.50	11.5	y	p	m	v	1.5	f	f	0	t	g	00000	4000	-
b	30.75	1.585	u	g	d	v	0.585	f	f	0	t	s	00000	0	-
b	36.67	2	u	g	i	v	0.25	f	f	0	t	g	00221	0	-
a	16.00	0.165	u	g	aa	v	1	f	t	02	t	g	00320	1	-
b	41.17	1.335	u	g	d	v	0.165	f	f	0	f	g	00168	0	-
a	19.50	0.165	u	g	q	v	0.04	f	f	0	t	g	00380	0	-
b	32.42	3	u	g	d	v	0.165	f	f	0	t	g	00120	0	-
a	36.75	4.71	u	g	ff	ff	0	f	f	0	f	g	00160	0	-
a	30.25	5.5	u	g	k	v	5.5	f	f	0	t	s	00100	0	-
b	23.08	2.5	u	g	ff	ff	0.085	f	f	0	t	g	00100	4208	-
b	26.83	0.54	u	g	k	ff	0	f	f	0	f	g	00100	0	-
b	16.92	0.335	y	p	k	v	0.29	f	f	0	f	s	00200	0	-
b	24.42	2	u	g	e	dd	0.165	f	t	02	f	g	00320	1300	-
b	42.83	1.25	u	g	m	v	13.875	f	t	01	t	g	00352	112	-
a	22.75	6.165	u	g	aa	v	0.165	f	f	0	f	g	00220	1000	-
b	39.42	1.71	y	p	m	v	0.165	f	f	0	f	s	00400	0	-
a	23.58	11.5	y	p	k	h	3	f	f	0	t	g	00020	16	-
b	21.42	0.75	y	p	r	n	0.75	f	f	0	t	g	00132	2	-
b	33.00	2.5	y	p	w	v	7	f	f	0	t	g	00280	0	-
b	26.33	13	u	g	e	dd	0	f	f	0	t	g	00140	1110	-
a	45.00	4.585	u	g	k	h	1	f	f	0	t	s	00240	0	-
b	26.25	1.54	u	g	w	v	0.125	f	f	0	f	g	00100	0	-
?	28.17	0.585	u	g	aa	v	0.04	f	f	0	f	g	00260	1004	-
a	20.83	0.5	y	p	e	dd	1	f	f	0	f	g	00260	0	-
b	28.67	14.5	u	g	d	v	0.125	f	f	0	f	g	00000	286	-
b	20.67	0.835	y	p	c	v	2	f	f	0	t	s	00240	0	-
b	34.42	1.335	u	g	i	bb	0.125	f	f	0	t	g	00440	4500	-
b	33.58	0.25	u	g	i	bb	4	f	f	0	t	s	00420	0	-
b	43.17	5	u	g	i	bb	2.25	f	f	0	t	g	00141	0	-
a	22.67	7	u	g	c	v	0.165	f	f	0	f	g	00160	0	-
a	24.33	2.5	y	p	i	bb	4.5	f	f	0	f	g	00200	456	-
a	56.83	4.25	y	p	ff	ff	5	f	f	0	t	g	00000	4	-
b	22.08	11.46	u	g	k	v	1.585	f	f	0	t	g	00100	1212	-
b	34.00	5.5	y	p	c	v	1.5	f	f	0	t	g	00060	0	-
b	22.58	1.5	y	p	aa	v	0.54	f	f	0	t	g	00120	67	-
b	21.17	0	u	g	c	v	0.5	f	f	0	t	s	00000	0	-
b	26.67	14.585	u	g	i	bb	0	f	f	0	t	g	00178	0	-
b	22.92	0.17	u	g	m	v	0.085	f	f	0	f	s	00000	0	-
b	15.17	7	u	g	e	v	1	f	f	0	f	g	00600	0	-
b	39.92	5	u	g	i	bb	0.21	f	f	0	f	g	00550	0	-
b	27.42	12.5	u	g	aa	bb	0.25	f	f	0	t	g	00720	0	-
b	24.75	0.54	u	g	m	v	1	f	f	0	t	g	00120	1	-
b	41.17	1.25	y	p	w	v	0.25	f	f	0	f	g	00000	195	-
a	33.08	1.625	u	g	d	v	0.54	f	f	0	t	g	00000	0	-
b	29.83	2.04	y	p	x	h	0.04	f	f	0	f	g	00128	1	-
a	23.58	0.585	y	p	ff	ff	0.125	f	f	0	f	g	00120	87	-
b	26.17	12.5	y	p	k	h	1.25	f	f	0	t	g	00000	17	-
b	31.00	2.085	u	g	c	v	0.085	f	f	0	f	g	00300	0	-
b	20.75	5.085	y	p	j	v	0.29	f	f	0	f	g	00140	184	-
b	28.92	0.375	u	g	c	v	0.29	f	f	0	f	g	00220	140	-
a	51.92	6.5	u	g	i	bb	3.085	f	f	0	t	g	00073	0	-
a	22.67	0.335	u	g	q	v	0.75	f	f	0	f	s	00160	0	-
b	34.00	5.085	y	p	i	bb	1.085	f	f	0	t	g	00480	0	-
a	69.50	6	u	g	ff	ff	0	f	f	0	f	s	00000	0	-
a	40.33	8.125	y	p	k	v	0.165	f	t	02	f	g	?	18	-
a	19.58	0.665	y	p	c	v	1	f	t	01	f	g	02000	2	-
b	16.00	3.125	u	g	w	v	0.085	f	t	01	f	g	00000	6	-
b	17.08	0.25	u	g	q	v	0.335	f	t	04	f	g	00160	8	-
b	31.25	2.835	u	g	ff	ff	0	f	t	05	f	g	00176	146	-
b	25.17	3	u	g	c	v	1.25	f	t	01	f	g	00000	22	-
a	22.67	0.79	u	g	i	v	0.085	f	f	0	f	g	00144	0	-
b	40.58	1.5	u	g	i	bb	0	f	f	0	f	s	00300	0	-
b	22.25	0.46	u	g	k	v	0.125	f	f	0	t	g	00280	55	-
a	22.25	1.25	y	p	ff	ff	3.25	f	f	0	f	g	00280	0	-
b	22.50	0.125	y	p	k	v	0.125	f	f	0	f	g	00200	70	-
b	23.58	1.79	u	g	c	v	0.54	f	f	0	t	g	00136	1	-
b	38.42	0.705	u	g	c	v	0.375	f	t	02	f	g	00225	500	-
a	26.58	2.54	y	p	ff	ff	0	f	f	0	t	g	00180	60	-
b	35.00	2.5	u	g	i	v	1	f	f	0	t	g	00210	0	-
b	20.42	1.085	u	g	q	v	1.5	f	f	0	f	g	00108	7	-
b	29.42	1.25	u	g	w	v	1.75	f	f	0	f	g	00200	0	-
b	26.17	0.835	u	g	cc	v	1.165	f	f	0	f	g	00100	0	-
b	33.67	2.165	u	g	c	v	1.5	f	f	0	f	p	00120	0	-
b	24.58	1.25	u	g	c	v	0.25	f	f	0	f	g	00110	0	-
a	27.67	2.04	u	g	w	v	0.25	f	f	0	t	g	00180	50	-
b	37.50	0.835	u	g	e	v	0.04	f	f	0	f	g	00120	5	-
b	49.17	2.29	u	g	ff	ff	0.29	f	f	0	f	g	00200	3	-
b	33.58	0.335	y	p	cc	v	0.085	f	f	0	f	g	00180	0	-
b	51.83	3	y	p	ff	ff	1.5	f	f	0	f	g	00180	4	-
b	22.92	3.165	y	p	c	v	0.165	f	f	0	f	g	00160	1058	-
b	21.83	1.54	u	g	k	v	0.085	f	f	0	t	g	00356	0	-
b	25.25	1	u	g	aa	v	0.5	f	f	0	f	g	00200	0	-
b	58.58	2.71	u	g	c	v	2.415	f	f	0	t	g	00320	0	-
b	19.00	0	y	p	ff	ff	0	f	t	04	f	g	00045	1	-
b	19.58	0.585	u	g	ff	ff	0	f	t	03	f	g	00350	769	-
a	53.33	0.165	u	g	ff	ff	0	f	f	0	t	s	00062	27	-
a	27.17	1.25	u	g	ff	ff	0	f	t	01	f	g	00092	300	-
b	25.92	0.875	u	g	k	v	0.375	f	t	02	t	g	00174	3	-
b	23.08	0	u	g	k	v	1	f	t	11	f	s	00000	0	-
b	39.58	5	u	g	ff	ff	0	f	t	02	f	g	00017	1	-
b	30.58	2.71	y	p	m	v	0.125	f	f	0	t	s	00080	0	-
b	17.25	3	u	g	k	v	0.04	f	f	0	t	g	00160	40	-
a	17.67	0	y	p	j	ff	0	f	f	0	f	g	00086	0	-
a	?	11.25	u	g	ff	ff	0	f	f	0	f	g	?	5200	-
b	16.50	0.125	u	g	c	v	0.165	f	f	0	f	g	00132	0	-
a	27.33	1.665	u	g	ff	ff	0	f	f	0	f	g	00340	1	-
b	31.25	1.125	u	g	ff	ff	0	f	t	01	f	g	00096	19	-
b	20.00	7	u	g	c	v	0.5	f	f	0	f	g	00000	0	-
b	?	3	y	p	i	bb	7	f	f	0	f	g	00000	1	-
b	39.50	1.625	u	g	c	v	1.5	f	f	0	f	g	00000	316	-
b	36.50	4.25	u	g	q	v	3.5	f	f	0	f	g	00454	50	-
?	29.75	0.665	u	g	w	v	0.25	f	f	0	t	g	00300	0	-
b	52.42	1.5	u	g	d	v	3.75	f	f	0	t	g	00000	350	-
b	36.17	18.125	u	g	w	v	0.085	f	f	0	f	g	00320	3552	-
b	34.58	0	?	?	?	?	0	f	f	0	f	p	?	0	-
b	29.67	0.75	y	p	c	v	0.04	f	f	0	f	g	00240	0	-
b	36.17	5.5	u	g	i	bb	5	f	f	0	f	g	00210	687	-
b	25.67	0.29	y	p	c	v	1.5	f	f	0	t	g	00160	0	-
a	24.50	2.415	y	p	c	v	0	f	f	0	f	g	00120	0	-
b	24.08	0.875	u	g	m	v	0.085	f	t	04	f	g	00254	1950	-
b	21.92	0.5	u	g	c	v	0.125	f	f	0	f	g	00360	0	-
a	36.58	0.29	u	g	ff	ff	0	f	t	10	f	g	00200	18	-
a	23.00	1.835	u	g	j	j	0	f	t	01	f	g	00200	53	-
a	27.58	3	u	g	m	v	2.79	f	t	01	t	g	00280	10	-
b	31.08	3.085	u	g	c	v	2.5	f	t	02	t	g	00160	41	-
a	30.42	1.375	u	g	w	h	0.04	f	t	03	f	g	00000	33	-
b	22.08	2.335	u	g	k	v	0.75	f	f	0	f	g	00180	0	-
b	16.33	4.085	u	g	i	h	0.415	f	f	0	t	g	00120	0	-
a	21.92	11.665	u	g	k	h	0.085	f	f	0	f	g	00320	5	-
b	21.08	4.125	y	p	i	h	0.04	f	f	0	f	g	00140	100	-
b	17.42	6.5	u	g	i	v	0.125	f	f	0	f	g	00060	100	-
b	19.17	4	y	p	i	v	1	f	f	0	t	g	00360	1000	-
b	20.67	0.415	u	g	c	v	0.125	f	f	0	f	g	00000	44	-
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b	23.58	0.835	u	g	i	h	0.085	f	f	0	t	g	00220	5	-
b	39.17	2.5	y	p	i	h	10	f	f	0	t	s	00200	0	-
b	22.75	11.5	u	g	i	v	0.415	f	f	0	f	g	00000	0	-
?	26.50	2.71	y	p	?	?	0.085	f	f	0	f	s	00080	0	-
a	16.92	0.5	u	g	i	v	0.165	f	t	06	t	g	00240	35	-
b	23.50	3.165	y	p	k	v	0.415	f	t	01	t	g	00280	80	-
a	17.33	9.5	u	g	aa	v	1.75	f	t	10	t	g	00000	10	-
b	23.75	0.415	y	p	c	v	0.04	f	t	02	f	g	00128	6	-
b	34.67	1.08	u	g	m	v	1.165	f	f	0	f	s	00028	0	-
b	74.83	19	y	p	ff	ff	0.04	f	t	02	f	g	00000	351	-
b	28.17	0.125	y	p	k	v	0.085	f	f	0	f	g	00216	2100	-
b	24.50	13.335	y	p	aa	v	0.04	f	f	0	t	g	00120	475	-
b	18.83	3.54	y	p	ff	ff	0	f	f	0	t	g	00180	1	-
?	45.33	1	u	g	q	v	0.125	f	f	0	t	g	00263	0	-

Data_Files_Arff/Data Files.zip

Data Files/autoPrice.arff

% % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! % % All nominal attributes and instances with missing values are deleted. % Price treated as the class attribute. % % As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction % using instance-based learning with encoding length selection. In Progress % in Connectionist-Based Information Systems. Singapore: Springer-Verlag. % % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! % % 1. Title: 1985 Auto Imports Database % % 2. Source Information: % -- Creator/Donor: Jeffrey C. Schlimmer ([email protected]) % -- Date: 19 May 1987 % -- Sources: % 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's % Automotive Yearbook. % 2) Personal Auto Manuals, Insurance Services Office, 160 Water % Street, New York, NY 10038 % 3) Insurance Collision Report, Insurance Institute for Highway % Safety, Watergate 600, Washington, DC 20037 % % 3. Past Usage: % -- Kibler,~D., Aha,~D.~W., \& Albert,~M. (1989). Instance-based prediction % of real-valued attributes. {\it Computational Intelligence}, {\it 5}, % 51--57. % -- Predicted price of car using all numeric and Boolean attributes % -- Method: an instance-based learning (IBL) algorithm derived from a % localized k-nearest neighbor algorithm. Compared with a % linear regression prediction...so all instances % with missing attribute values were discarded. This resulted with % a training set of 159 instances, which was also used as a test % set (minus the actual instance during testing). % -- Results: Percent Average Deviation Error of Prediction from Actual % -- 11.84% for the IBL algorithm % -- 14.12% for the resulting linear regression equation % % 4. Relevant Information: % -- Description % This data set consists of three types of entities: (a) the % specification of an auto in terms of various characteristics, (b) % its assigned insurance risk rating, (c) its normalized losses in use % as compared to other cars. The second rating corresponds to the % degree to which the auto is more risky than its price indicates. % Cars are initially assigned a risk factor symbol associated with its % price. Then, if it is more risky (or less), this symbol is % adjusted by moving it up (or down) the scale. Actuarians call this % process "symboling". A value of +3 indicates that the auto is % risky, -3 that it is probably pretty safe. % % The third factor is the relative average loss payment per insured % vehicle year. This value is normalized for all autos within a % particular size classification (two-door small, station wagons, % sports/speciality, etc...), and represents the average loss per car % per year. % % -- Note: Several of the attributes in the database could be used as a % "class" attribute. % % 5. Number of Instances: 205 % % 6. Number of Attributes: 26 total % -- 15 continuous % -- 1 integer % -- 10 nominal % % 7. Attribute Information: % Attribute: Attribute Range: % ------------------ ----------------------------------------------- % 1. symboling: -3, -2, -1, 0, 1, 2, 3. % 2. normalized-losses: continuous from 65 to 256. % 3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, % isuzu, jaguar, mazda, mercedes-benz, mercury, % mitsubishi, nissan, peugot, plymouth, porsche, % renault, saab, subaru, toyota, volkswagen, volvo % 4. fuel-type: diesel, gas. % 5. aspiration: std, turbo. % 6. num-of-doors: four, two. % 7. body-style: hardtop, wagon, sedan, hatchback, convertible. % 8. drive-wheels: 4wd, fwd, rwd. % 9. engine-location: front, rear. % 10. wheel-base: continuous from 86.6 120.9. % 11. length: continuous from 141.1 to 208.1. % 12. width: continuous from 60.3 to 72.3. % 13. height: continuous from 47.8 to 59.8. % 14. curb-weight: continuous from 1488 to 4066. % 15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor. % 16. num-of-cylinders: eight, five, four, six, three, twelve, two. % 17. engine-size: continuous from 61 to 326. % 18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi. % 19. bore: continuous from 2.54 to 3.94. % 20. stroke: continuous from 2.07 to 4.17. % 21. compression-ratio: continuous from 7 to 23. % 22. horsepower: continuous from 48 to 288. % 23. peak-rpm: continuous from 4150 to 6600. % 24. city-mpg: continuous from 13 to 49. % 25. highway-mpg: continuous from 16 to 54. % 26. price: continuous from 5118 to 45400. % % 8. Missing Attribute Values: (denoted by "?") % Attribute #: Number of instances missing a value: % 2. 41 % 6. 2 % 19. 4 % 20. 4 % 22. 2 % 23. 2 % 26. 4% % @relation 'autoPrice.names' @attribute symboling real @attribute normalized-losses real @attribute wheel-base real @attribute length real @attribute width real @attribute height real @attribute curb-weight real @attribute engine-size real @attribute bore real @attribute stroke real @attribute compression-ratio real @attribute horsepower real @attribute peak-rpm real @attribute city-mpg real @attribute highway-mpg real @attribute class real @data 2,164,99.8,176.6,66.2,54.3,2337,109,3.19,3.4,10,102,5500,24,30,13950 2,164,99.4,176.6,66.4,54.3,2824,136,3.19,3.4,8,115,5500,18,22,17450 1,158,105.8,192.7,71.4,55.7,2844,136,3.19,3.4,8.5,110,5500,19,25,17710 1,158,105.8,192.7,71.4,55.9,3086,131,3.13,3.4,8.3,140,5500,17,20,23875 2,192,101.2,176.8,64.8,54.3,2395,108,3.5,2.8,8.8,101,5800,23,29,16430 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0,91,95.7,166.3,64.4,53,2094,98,3.19,3.03,9,70,4800,38,47,7738 0,91,95.7,166.3,64.4,52.8,2122,98,3.19,3.03,9,70,4800,28,34,8358 0,91,95.7,166.3,64.4,52.8,2140,98,3.19,3.03,9,70,4800,28,34,9258 1,168,94.5,168.7,64,52.6,2169,98,3.19,3.03,9,70,4800,29,34,8058 1,168,94.5,168.7,64,52.6,2204,98,3.19,3.03,9,70,4800,29,34,8238 1,168,94.5,168.7,64,52.6,2265,98,3.24,3.08,9.4,112,6600,26,29,9298 1,168,94.5,168.7,64,52.6,2300,98,3.24,3.08,9.4,112,6600,26,29,9538 2,134,98.4,176.2,65.6,52,2540,146,3.62,3.5,9.3,116,4800,24,30,8449 2,134,98.4,176.2,65.6,52,2536,146,3.62,3.5,9.3,116,4800,24,30,9639 2,134,98.4,176.2,65.6,52,2551,146,3.62,3.5,9.3,116,4800,24,30,9989 2,134,98.4,176.2,65.6,52,2679,146,3.62,3.5,9.3,116,4800,24,30,11199 2,134,98.4,176.2,65.6,52,2714,146,3.62,3.5,9.3,116,4800,24,30,11549 2,134,98.4,176.2,65.6,53,2975,146,3.62,3.5,9.3,116,4800,24,30,17669 -1,65,102.4,175.6,66.5,54.9,2326,122,3.31,3.54,8.7,92,4200,29,34,8948 -1,65,102.4,175.6,66.5,54.9,2480,110,3.27,3.35,22.5,73,4500,30,33,10698 -1,65,102.4,175.6,66.5,53.9,2414,122,3.31,3.54,8.7,92,4200,27,32,9988 -1,65,102.4,175.6,66.5,54.9,2414,122,3.31,3.54,8.7,92,4200,27,32,10898 -1,65,102.4,175.6,66.5,53.9,2458,122,3.31,3.54,8.7,92,4200,27,32,11248 3,197,102.9,183.5,67.7,52,2976,171,3.27,3.35,9.3,161,5200,20,24,16558 3,197,102.9,183.5,67.7,52,3016,171,3.27,3.35,9.3,161,5200,19,24,15998 -1,90,104.5,187.8,66.5,54.1,3131,171,3.27,3.35,9.2,156,5200,20,24,15690 2,122,97.3,171.7,65.5,55.7,2261,97,3.01,3.4,23,52,4800,37,46,7775 2,122,97.3,171.7,65.5,55.7,2209,109,3.19,3.4,9,85,5250,27,34,7975 2,94,97.3,171.7,65.5,55.7,2264,97,3.01,3.4,23,52,4800,37,46,7995 2,94,97.3,171.7,65.5,55.7,2212,109,3.19,3.4,9,85,5250,27,34,8195 2,94,97.3,171.7,65.5,55.7,2275,109,3.19,3.4,9,85,5250,27,34,8495 2,94,97.3,171.7,65.5,55.7,2319,97,3.01,3.4,23,68,4500,37,42,9495 2,94,97.3,171.7,65.5,55.7,2300,109,3.19,3.4,10,100,5500,26,32,9995 3,256,94.5,165.7,64,51.4,2221,109,3.19,3.4,8.5,90,5500,24,29,9980 -2,103,104.3,188.8,67.2,56.2,2912,141,3.78,3.15,9.5,114,5400,23,28,12940 -1,74,104.3,188.8,67.2,57.5,3034,141,3.78,3.15,9.5,114,5400,23,28,13415 -2,103,104.3,188.8,67.2,56.2,2935,141,3.78,3.15,9.5,114,5400,24,28,15985 -1,74,104.3,188.8,67.2,57.5,3042,141,3.78,3.15,9.5,114,5400,24,28,16515 -2,103,104.3,188.8,67.2,56.2,3045,130,3.62,3.15,7.5,162,5100,17,22,18420 -1,74,104.3,188.8,67.2,57.5,3157,130,3.62,3.15,7.5,162,5100,17,22,18950 -1,95,109.1,188.8,68.9,55.5,2952,141,3.78,3.15,9.5,114,5400,23,28,16845 -1,95,109.1,188.8,68.8,55.5,3049,141,3.78,3.15,8.7,160,5300,19,25,19045 -1,95,109.1,188.8,68.9,55.5,3012,173,3.58,2.87,8.8,134,5500,18,23,21485 -1,95,109.1,188.8,68.9,55.5,3217,145,3.01,3.4,23,106,4800,26,27,22470 -1,95,109.1,188.8,68.9,55.5,3062,141,3.78,3.15,9.5,114,5400,19,25,22625

Data Files/baskball.arff

% Dataset from Smoothing Methods in Statistics % (ftp stat.cmu.edu/datasets) % % Simonoff, J.S. (1996). Smoothing Methods in Statistics. New York: Springer-Verlag. % % % Points scored per minute is being treated as % the class attribute. @relation baskball @attribute assists_per_minute real @attribute height integer @attribute time_played real @attribute age integer @attribute points_per_minute real @data 0.0888,201,36.02,28,0.5885 0.1399,198,39.32,30,0.8291 0.0747,198,38.8,26,0.4974 0.0983,191,40.71,30,0.5772 0.1276,196,38.4,28,0.5703 0.1671,201,34.1,31,0.5835 0.1906,193,36.2,30,0.5276 0.1061,191,36.75,27,0.5523 0.2446,185,38.43,29,0.4007 0.167,203,33.54,24,0.477 0.2485,188,35.01,27,0.4313 0.1227,198,36.67,29,0.4909 0.124,185,33.88,24,0.5668 0.1461,191,35.59,30,0.5113 0.2315,191,38.01,28,0.3788 0.0494,193,32.38,32,0.559 0.1107,196,35.22,25,0.4799 0.2521,183,31.73,29,0.5735 0.1007,193,28.81,34,0.6318 0.1067,196,35.6,23,0.4326 0.1956,188,35.28,32,0.428 0.1828,191,29.54,28,0.4401 0.1627,196,31.35,28,0.5581 0.1403,198,33.5,23,0.4866 0.1563,193,34.56,32,0.5267 0.2681,183,39.53,27,0.5439 0.1236,196,26.7,34,0.4419 0.13,188,30.77,26,0.3998 0.0896,198,25.67,30,0.4325 0.2071,178,36.22,30,0.4086 0.2244,185,36.55,23,0.4624 0.3437,185,34.91,31,0.4325 0.1058,191,28.35,28,0.4903 0.2326,185,33.53,27,0.4802 0.1577,193,31.07,25,0.4345 0.2327,185,36.52,32,0.4819 0.1256,196,27.87,29,0.6244 0.107,198,24.31,34,0.3991 0.1343,193,31.26,28,0.4414 0.0586,196,22.18,23,0.4013 0.2383,185,35.25,26,0.3801 0.1006,198,22.87,30,0.3498 0.2164,193,24.49,32,0.3185 0.1485,198,23.57,27,0.3097 0.227,191,31.72,27,0.4319 0.1649,188,27.9,25,0.3799 0.1188,191,22.74,24,0.4091 0.194,193,20.62,27,0.3588 0.2495,185,30.46,25,0.4727 0.2378,185,32.38,27,0.3212 0.1592,191,25.75,31,0.3418 0.2069,170,33.84,30,0.4285 0.2084,185,27.83,25,0.3917 0.0877,193,21.67,26,0.5769 0.101,193,21.79,24,0.4773 0.0942,201,20.17,26,0.4512 0.055,193,29.07,31,0.3096 0.1071,196,24.28,24,0.3089 0.0728,193,19.24,27,0.4573 0.2771,180,27.07,28,0.3214 0.0528,196,18.95,22,0.5437 0.213,188,21.59,30,0.4121 0.1356,193,13.27,31,0.2185 0.1043,196,16.3,23,0.3313 0.113,191,23.01,25,0.3302 0.1477,196,20.31,31,0.4677 0.1317,188,17.46,33,0.2406 0.2187,191,21.95,28,0.3007 0.2127,188,14.57,37,0.2471 0.2547,160,34.55,28,0.2894 0.1591,191,22,24,0.3682 0.0898,196,13.37,34,0.389 0.2146,188,20.51,24,0.512 0.1871,183,19.78,28,0.4449 0.1528,191,16.36,33,0.4035 0.156,191,16.03,23,0.2683 0.2348,188,24.27,26,0.2719 0.1623,180,18.49,28,0.3408 0.1239,180,17.76,26,0.4393 0.2178,185,13.31,25,0.3004 0.1608,185,17.41,26,0.3503 0.0805,193,13.67,25,0.4388 0.1776,193,17.46,27,0.2578 0.1668,185,14.38,35,0.2989 0.1072,188,12.12,31,0.4455 0.1821,185,12.63,25,0.3087 0.188,180,12.24,30,0.3678 0.1167,196,12,24,0.3667 0.2617,185,24.46,27,0.3189 0.1994,188,20.06,27,0.4187 0.1706,170,17,25,0.5059 0.1554,183,11.58,24,0.3195 0.2282,185,10.08,24,0.2381 0.1778,185,18.56,23,0.2802 0.1863,185,11.81,23,0.381 0.1014,193,13.81,32,0.1593

Data Files/bolts.arff

Data Files/cloud.arff

% Data from StatLib (ftp stat.cmu.edu/datasets) % % These data are those collected in a cloud-seeding experiment in Tasmania % between mid-1964 and January 1971. Their analysis, using regression % techniques and permutation tests, is discussed in: % % Miller, A.J., Shaw, D.E., Veitch, L.G. & Smith, E.J. (1979). % `Analyzing the results of a cloud-seeding experiment in Tasmania', % Communications in Statistics - Theory & Methods, vol.A8(10), % 1017-1047. % % The rainfalls are period rainfalls in inches. TE and TW are the east and % west target areas respectively, while NC, SC and NWC are the corresponding % rainfalls in the north, south and north-west control areas respectively. % S = seeded, U = unseeded. % % Rain in eastern target region is being treated % as the class attribute. (Attribute for rain % in the western target region has been deleted.) @relation cloud @attribute period integer @attribute seeded { S, U} @attribute season { AUTUMN, WINTER, SPRING, SUMMER} @attribute NC real @attribute SC real @attribute NWC real @attribute TE real @data 1,S,AUTUMN,1.65,1.8,3.33,1.69 2,U,AUTUMN,1.09,0.79,1.59,0.74 3,S,WINTER,2.39,0.36,2.06,0.81 4,U,WINTER,2.96,1.27,4.05,1.44 5,S,WINTER,4.16,2.16,6,2.48 6,U,WINTER,2.76,0.87,4.17,0.84 7,U,WINTER,1.08,0.85,3.45,0.37 8,S,WINTER,0.26,0.47,0.9,0.37 9,U,SPRING,2.53,1.08,3.65,1.33 10,S,SPRING,2.76,3.1,5.06,3.38 11,S,SPRING,1.07,0.64,1.95,0.69 12,U,SPRING,1.42,1.08,1.22,1.42 13,S,SPRING,0.24,0.44,0.94,0.44 14,U,SPRING,0.7,0.67,0.94,0.76 15,S,SUMMER,0.97,1.66,2.21,1.13 16,U,SUMMER,1.06,1.13,1.46,0.88 45,S,SUMMER,0.13,0.27,0.35,0.17 46,U,SUMMER,0.1,0.3,0.34,0.25 47,U,SUMMER,0.38,0.58,0.67,0.78 48,S,SUMMER,0.45,0.43,0.44,0.4 49,S,AUTUMN,0.42,0.47,0.53,0.52 50,U,AUTUMN,2.24,4.02,2.52,2.73 51,U,AUTUMN,0.52,1.32,2.18,0.9 52,S,AUTUMN,0.94,1.59,1.73,1.62 53,U,AUTUMN,1.19,0.85,2.31,0.93 54,S,AUTUMN,0.76,0.71,1.28,0.63 55,S,WINTER,0.13,0.59,0.91,0.42 56,U,WINTER,1.5,0.24,1.15,0.64 57,U,WINTER,1.03,0.22,1.88,0.3 58,S,WINTER,1.87,0.58,2.97,0.88 59,U,WINTER,1.85,1.36,2.17,0.76 60,S,WINTER,2.04,0.71,2.22,1.25 61,U,WINTER,1.44,1,1.64,1.08 62,S,WINTER,1.46,1.48,0.4,1.11 63,S,SPRING,5.08,1.77,4.2,3.43 64,U,SPRING,0.66,0.73,0.91,0.54 65,S,SPRING,0.49,0.55,0.51,0.39 66,U,SPRING,3.27,2.68,3.6,2.53 67,U,SPRING,1.33,0.43,2.18,0.81 68,S,SPRING,0.25,0.46,0.89,0.39 69,S,SUMMER,0.69,0.49,0.69,0.86 70,U,SUMMER,2.12,0.95,1.82,2.16 95,U,SPRING,1.45,1.47,2.2,1.7 96,S,SPRING,2.13,1.13,2.33,1.22 97,S,SPRING,0.02,0.08,0.24,0.07 98,U,SPRING,0.36,0.87,0.57,0.49 99,U,SPRING,0.72,0.99,0.98,0.71 100,S,SPRING,1.02,1.89,2.47,1.67 101,U,SUMMER,0.18,1.42,0.71,0.73 102,S,SUMMER,1.83,1.82,3.11,1.79 103,U,SUMMER,0.08,0.4,0.57,0.19 104,S,SUMMER,0,0.04,0.04,0 105,S,SUMMER,0.83,0.38,0.7,0.44 106,U,SUMMER,0.01,0.44,0.66,0.31 107,S,SUMMER,2.65,0.85,1.48,0.96 108,U,SUMMER,1.27,1.39,1.2,1.04 109,S,AUTUMN,0.01,0.23,0.1,0.05 110,U,AUTUMN,0.35,0.75,0.2,0.04 111,S,AUTUMN,1.8,1.62,3.02,1.83 112,U,AUTUMN,4.44,1.05,3.59,2.24 113,S,AUTUMN,2.84,2.44,4.48,2.5 114,U,AUTUMN,2.05,1.3,4.04,1.1 115,S,AUTUMN,3.01,1.66,4.56,1.83 116,U,AUTUMN,2.58,1.21,3.95,1.41 117,U,WINTER,2.22,0.61,2.68,0.74 118,S,WINTER,0.07,2.26,2.08,1.09 119,S,WINTER,1.62,1.16,2.87,0.79 120,U,WINTER,4.34,3.29,6.4,4.06 121,U,WINTER,1.03,0.58,1.77,0.4 122,S,WINTER,1.5,0.41,2.56,0.76 123,S,SPRING,1.52,1.62,2.86,1.53 124,U,SPRING,0.37,1.25,1.74,0.56 125,U,SPRING,2.14,1,4.39,1.74 126,S,SPRING,2.36,1.53,3.03,1.59 127,U,SPRING,1.71,2.03,3.24,1.91 128,S,SPRING,2.12,2.77,4.44,2.09 129,U,SUMMER,1.38,2.11,3.01,1.59 130,S,SUMMER,0.21,1.41,0.8,0.66 131,U,SUMMER,0.48,0.59,0.68,0.68 132,S,SUMMER,0.01,0.65,0.48,0.46 133,S,SUMMER,0.15,0.13,0.42,0.22 134,U,SUMMER,1.32,0.57,1.54,1.11 135,S,SUMMER,2.26,1.04,1.27,1.76 136,U,SUMMER,5.95,3.97,5.37,5.12 171,U,AUTUMN,0.19,0.28,0.7,0.12 172,S,AUTUMN,0.31,0.23,0.83,0.37 173,S,AUTUMN,1.44,3.14,0.86,4.97 174,U,AUTUMN,0.3,0.72,1.38,0.57 175,S,AUTUMN,0.11,0.14,0.58,0.13 176,U,AUTUMN,3.66,1.84,5.36,2.47 177,U,AUTUMN,1.14,0.81,2.09,1.01 178,S,AUTUMN,1.3,0.34,2.45,0.55 179,S,WINTER,0.05,0.38,0.9,0.24 180,U,WINTER,1.84,1.73,2.33,2.36 181,S,WINTER,4.24,1.67,5.48,2.35 182,U,WINTER,1.99,1.9,3.67,2.23 183,U,WINTER,2.44,1.52,4.01,1.16 184,S,WINTER,2.21,2.36,3.25,1.63 185,S,WINTER,0.8,2.25,2.79,1.08 186,U,WINTER,9.42,3.6,7.84,6 187,S,SPRING,2.74,3.03,6.39,2.67 188,U,SPRING,0,0.19,0.06,0.36 189,S,SPRING,0.96,0.64,1.24,0.58 190,U,SPRING,1.38,1.86,2.91,1.36 191,S,SPRING,1.22,2.28,1.58,1.17 192,U,SPRING,2.46,2.47,2.39,2.37 193,S,SPRING,0.05,0.02,0.09,0.02 194,U,SPRING,0.61,0.87,1.35,0.92

Data Files/hungarian.arff

% Publication Request: % >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> % This file describes the contents of the heart-disease directory. % % This directory contains 4 databases concerning heart disease diagnosis. % All attributes are numeric-valued. The data was collected from the % four following locations: % % 1. Cleveland Clinic Foundation (cleveland.data) % 2. Hungarian Institute of Cardiology, Budapest (hungarian.data) % 3. V.A. Medical Center, Long Beach, CA (long-beach-va.data) % 4. University Hospital, Zurich, Switzerland (switzerland.data) % % Each database has the same instance format. While the databases have 76 % raw attributes, only 14 of them are actually used. Thus I've taken the % liberty of making 2 copies of each database: one with all the attributes % and 1 with the 14 attributes actually used in past experiments. % % The authors of the databases have requested: % % ...that any publications resulting from the use of the data include the % names of the principal investigator responsible for the data collection % at each institution. They would be: % % 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. % 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. % 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. % 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: % Robert Detrano, M.D., Ph.D. % % Thanks in advance for abiding by this request. % % David Aha % July 22, 1988 % >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> % % 1. Title: Heart Disease Databases % % 2. Source Information: % (a) Creators: % -- 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. % -- 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. % -- 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. % -- 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: % Robert Detrano, M.D., Ph.D. % (b) Donor: David W. Aha ([email protected]) (714) 856-8779 % (c) Date: July, 1988 % % 3. Past Usage: % 1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J., % Sandhu,~S., Guppy,~K., Lee,~S., \& Froelicher,~V. (1989). {\it % International application of a new probability algorithm for the % diagnosis of coronary artery disease.} {\it American Journal of % Cardiology}, {\it 64},304--310. % -- International Probability Analysis % -- Address: Robert Detrano, M.D. % Cardiology 111-C % V.A. Medical Center % 5901 E. 7th Street % Long Beach, CA 90028 % -- Results in percent accuracy: (for 0.5 probability threshold) % Data Name: CDF CADENZA % -- Hungarian 77 74 % Long beach 79 77 % Swiss 81 81 % -- Approximately a 77% correct classification accuracy with a % logistic-regression-derived discriminant function % 2. David W. Aha & Dennis Kibler % -- % % % -- Instance-based prediction of heart-disease presence with the % Cleveland database % -- NTgrowth: 77.0% accuracy % -- C4: 74.8% accuracy % 3. John Gennari % -- Gennari, J.~H., Langley, P, \& Fisher, D. (1989). Models of % incremental concept formation. {\it Artificial Intelligence, 40}, % 11--61. % -- Results: % -- The CLASSIT conceptual clustering system achieved a 78.9% accuracy % on the Cleveland database. % % 4. Relevant Information: % This database contains 76 attributes, but all published experiments % refer to using a subset of 14 of them. In particular, the Cleveland % database is the only one that has been used by ML researchers to % this date. The "goal" field refers to the presence of heart disease % in the patient. It is integer valued from 0 (no presence) to 4. % Experiments with the Cleveland database have concentrated on simply % attempting to distinguish presence (values 1,2,3,4) from absence (value % 0). % % The names and social security numbers of the patients were recently % removed from the database, replaced with dummy values. % % One file has been "processed", that one containing the Cleveland % database. All four unprocessed files also exist in this directory. % % 5. Number of Instances: % Database: # of instances: % Cleveland: 303 % Hungarian: 294 % Switzerland: 123 % Long Beach VA: 200 % % 6. Number of Attributes: 76 (including the predicted attribute) % % 7. Attribute Information: % -- Only 14 used % -- 1. #3 (age) % -- 2. #4 (sex) % -- 3. #9 (cp) % -- 4. #10 (trestbps) % -- 5. #12 (chol) % -- 6. #16 (fbs) % -- 7. #19 (restecg) % -- 8. #32 (thalach) % -- 9. #38 (exang) % -- 10. #40 (oldpeak) % -- 11. #41 (slope) % -- 12. #44 (ca) % -- 13. #51 (thal) % -- 14. #58 (num) (the predicted attribute) % % -- Complete attribute documentation: % 1 id: patient identification number % 2 ccf: social security number (I replaced this with a dummy value of 0) % 3 age: age in years % 4 sex: sex (1 = male; 0 = female) % 5 painloc: chest pain location (1 = substernal; 0 = otherwise) % 6 painexer (1 = provoked by exertion; 0 = otherwise) % 7 relrest (1 = relieved after rest; 0 = otherwise) % 8 pncaden (sum of 5, 6, and 7) % 9 cp: chest pain type % -- Value 1: typical angina % -- Value 2: atypical angina % -- Value 3: non-anginal pain % -- Value 4: asymptomatic % 10 trestbps: resting blood pressure (in mm Hg on admission to the % hospital) % 11 htn % 12 chol: serum cholestoral in mg/dl % 13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker) % 14 cigs (cigarettes per day) % 15 years (number of years as a smoker) % 16 fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) % 17 dm (1 = history of diabetes; 0 = no such history) % 18 famhist: family history of coronary artery disease (1 = yes; 0 = no) % 19 restecg: resting electrocardiographic results % -- Value 0: normal % -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST % elevation or depression of > 0.05 mV) % -- Value 2: showing probable or definite left ventricular hypertrophy % by Estes' criteria % 20 ekgmo (month of exercise ECG reading) % 21 ekgday(day of exercise ECG reading) % 22 ekgyr (year of exercise ECG reading) % 23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no) % 24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no) % 25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no) % 26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no) % 27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no) % 28 proto: exercise protocol % 1 = Bruce % 2 = Kottus % 3 = McHenry % 4 = fast Balke % 5 = Balke % 6 = Noughton % 7 = bike 150 kpa min/min (Not sure if "kpa min/min" is what was % written!) % 8 = bike 125 kpa min/min % 9 = bike 100 kpa min/min % 10 = bike 75 kpa min/min % 11 = bike 50 kpa min/min % 12 = arm ergometer % 29 thaldur: duration of exercise test in minutes % 30 thaltime: time when ST measure depression was noted % 31 met: mets achieved % 32 thalach: maximum heart rate achieved % 33 thalrest: resting heart rate % 34 tpeakbps: peak exercise blood pressure (first of 2 parts) % 35 tpeakbpd: peak exercise blood pressure (second of 2 parts) % 36 dummy % 37 trestbpd: resting blood pressure % 38 exang: exercise induced angina (1 = yes; 0 = no) % 39 xhypo: (1 = yes; 0 = no) % 40 oldpeak = ST depression induced by exercise relative to rest % 41 slope: the slope of the peak exercise ST segment % -- Value 1: upsloping % -- Value 2: flat % -- Value 3: downsloping % 42 rldv5: height at rest % 43 rldv5e: height at peak exercise % 44 ca: number of major vessels (0-3) colored by flourosopy % 45 restckm: irrelevant % 46 exerckm: irrelevant % 47 restef: rest raidonuclid (sp?) ejection fraction % 48 restwm: rest wall (sp?) motion abnormality % 0 = none % 1 = mild or moderate % 2 = moderate or severe % 3 = akinesis or dyskmem (sp?) % 49 exeref: exercise radinalid (sp?) ejection fraction % 50 exerwm: exercise wall (sp?) motion % 51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect % 52 thalsev: not used % 53 thalpul: not used % 54 earlobe: not used % 55 cmo: month of cardiac cath (sp?) (perhaps "call") % 56 cday: day of cardiac cath (sp?) % 57 cyr: year of cardiac cath (sp?) % 58 num: diagnosis of heart disease (angiographic disease status) % -- Value 0: < 50% diameter narrowing % -- Value 1: > 50% diameter narrowing % (in any major vessel: attributes 59 through 68 are vessels) % 59 lmt % 60 ladprox % 61 laddist % 62 diag % 63 cxmain % 64 ramus % 65 om1 % 66 om2 % 67 rcaprox % 68 rcadist % 69 lvx1: not used % 70 lvx2: not used % 71 lvx3: not used % 72 lvx4: not used % 73 lvf: not used % 74 cathef: not used % 75 junk: not used % 76 name: last name of patient % (I replaced this with the dummy string "name") % % 9. Missing Attribute Values: Several. Distinguished with value -9.0. % % 10. Class Distribution: % Database: 0 1 2 3 4 Total % Cleveland: 164 55 36 35 13 303 % Hungarian: 188 37 26 28 15 294 % Switzerland: 8 48 32 30 5 123 % Long Beach VA: 51 56 41 42 10 200 % @relation 'hungarian' @attribute 'age' real @attribute 'sex' { 0, 1} @attribute 'cp' { 1, 4, 3, 2} @attribute 'trestbps' real @attribute 'chol' real @attribute 'fbs' { 1, 0} @attribute 'restecg' { 2, 0, 1} @attribute 'thalach' real @attribute 'exang' { 0, 1} @attribute 'oldpeak' real @attribute 'slope' { 3, 2, 1} @attribute 'ca' real @attribute 'thal' { 6, 3, 7} @attribute 'num' real @data 28,1,2,130,132,0,2,185,0,0,?,?,?,0 29,1,2,120,243,0,0,160,0,0,?,?,?,0 29,1,2,140,?,0,0,170,0,0,?,?,?,0 30,0,1,170,237,0,1,170,0,0,?,?,6,0 31,0,2,100,219,0,1,150,0,0,?,?,?,0 32,0,2,105,198,0,0,165,0,0,?,?,?,0 32,1,2,110,225,0,0,184,0,0,?,?,?,0 32,1,2,125,254,0,0,155,0,0,?,?,?,0 33,1,3,120,298,0,0,185,0,0,?,?,?,0 34,0,2,130,161,0,0,190,0,0,?,?,?,0 34,1,2,150,214,0,1,168,0,0,?,?,?,0 34,1,2,98,220,0,0,150,0,0,?,?,?,0 35,0,1,120,160,0,1,185,0,0,?,?,?,0 35,0,4,140,167,0,0,150,0,0,?,?,?,0 35,1,2,120,308,0,2,180,0,0,?,?,?,0 35,1,2,150,264,0,0,168,0,0,?,?,?,0 36,1,2,120,166,0,0,180,0,0,?,?,?,0 36,1,3,112,340,0,0,184,0,1,2,?,3,0 36,1,3,130,209,0,0,178,0,0,?,?,?,0 36,1,3,150,160,0,0,172,0,0,?,?,?,0 37,0,2,120,260,0,0,130,0,0,?,?,?,0 37,0,3,130,211,0,0,142,0,0,?,?,?,0 37,0,4,130,173,0,1,184,0,0,?,?,?,0 37,1,2,130,283,0,1,98,0,0,?,?,?,0 37,1,3,130,194,0,0,150,0,0,?,?,?,0 37,1,4,120,223,0,0,168,0,0,?,?,3,0 37,1,4,130,315,0,0,158,0,0,?,?,?,0 38,0,2,120,275,?,0,129,0,0,?,?,?,0 38,1,2,140,297,0,0,150,0,0,?,?,?,0 38,1,3,145,292,0,0,130,0,0,?,?,?,0 39,0,3,110,182,0,1,180,0,0,?,?,?,0 39,1,2,120,?,0,1,146,0,2,1,?,?,0 39,1,2,120,200,0,0,160,1,1,2,?,?,0 39,1,2,120,204,0,0,145,0,0,?,?,?,0 39,1,2,130,?,0,0,120,0,0,?,?,?,0 39,1,2,190,241,0,0,106,0,0,?,?,?,0 39,1,3,120,339,0,0,170,0,0,?,?,?,0 39,1,3,160,147,1,0,160,0,0,?,?,?,0 39,1,4,110,273,0,0,132,0,0,?,?,?,0 39,1,4,130,307,0,0,140,0,0,?,?,?,0 40,1,2,130,275,0,0,150,0,0,?,?,?,0 40,1,2,140,289,0,0,172,0,0,?,?,?,0 40,1,3,130,215,0,0,138,0,0,?,?,?,0 40,1,3,130,281,0,0,167,0,0,?,?,?,0 40,1,3,140,?,0,0,188,0,0,?,?,?,0 41,0,2,110,250,0,1,142,0,0,?,?,?,0 41,0,2,125,184,0,0,180,0,0,?,?,?,0 41,0,2,130,245,0,0,150,0,0,?,?,?,0 41,1,2,120,291,0,1,160,0,0,?,?,?,0 41,1,2,120,295,0,0,170,0,0,?,?,?,0 41,1,2,125,269,0,0,144,0,0,?,?,?,0 41,1,4,112,250,0,0,142,0,0,?,?,?,0 42,0,3,115,211,0,1,137,0,0,?,?,?,0 42,1,2,120,196,0,0,150,0,0,?,?,?,0 42,1,2,120,198,0,0,155,0,0,?,?,?,0 42,1,2,150,268,0,0,136,0,0,?,?,?,0 42,1,3,120,228,0,0,152,1,1.5,2,?,?,0 42,1,3,160,147,0,0,146,0,0,?,?,?,0 42,1,4,140,358,0,0,170,0,0,?,?,?,0 43,0,1,100,223,0,0,142,0,0,?,?,?,0 43,0,2,120,201,0,0,165,0,0,?,?,?,0 43,0,2,120,215,0,1,175,0,0,?,?,?,0 43,0,2,120,249,0,1,176,0,0,?,?,?,0 43,0,2,120,266,0,0,118,0,0,?,?,?,0 43,0,2,150,186,0,0,154,0,0,?,?,?,0 43,0,3,150,?,0,0,175,0,0,?,?,3,0 43,1,2,142,207,0,0,138,0,0,?,?,?,0 44,0,4,120,218,0,1,115,0,0,?,?,?,0 44,1,2,120,184,0,0,142,0,1,2,?,?,0 44,1,2,130,215,0,0,135,0,0,?,?,?,0 44,1,4,150,412,0,0,170,0,0,?,?,?,0 45,0,2,130,237,0,0,170,0,0,?,?,?,0 45,0,2,180,?,0,0,180,0,0,?,?,?,0 45,0,4,132,297,0,0,144,0,0,?,?,?,0 45,1,2,140,224,1,0,122,0,0,?,?,?,0 45,1,3,135,?,0,0,110,0,0,?,?,?,0 45,1,4,120,225,0,0,140,0,0,?,?,?,0 45,1,4,140,224,0,0,144,0,0,?,?,?,0 46,0,4,130,238,0,0,90,0,0,?,?,?,0 46,1,2,140,275,0,0,165,1,0,?,?,?,0 46,1,3,120,230,0,0,150,0,0,?,?,?,0 46,1,3,150,163,?,0,116,0,0,?,?,?,0 46,1,4,110,238,0,1,140,1,1,2,?,3,0 46,1,4,110,240,0,1,140,0,0,?,?,3,0 46,1,4,180,280,0,1,120,0,0,?,?,?,0 47,0,2,140,257,0,0,135,0,1,1,?,?,0 47,0,3,130,?,0,0,145,0,2,2,?,?,0 47,1,1,110,249,0,0,150,0,0,?,?,?,0 47,1,2,160,263,0,0,174,0,0,?,?,?,0 47,1,4,140,276,1,0,125,1,0,?,?,?,0 48,0,2,?,308,0,1,?,?,2,1,?,?,0 48,0,2,120,?,1,1,148,0,0,?,?,?,0 48,0,2,120,284,0,0,120,0,0,?,?,?,0 48,0,3,120,195,0,0,125,0,0,?,?,?,0 48,0,4,108,163,0,0,175,0,2,1,?,?,0 48,0,4,120,254,0,1,110,0,0,?,?,?,0 48,0,4,150,227,0,0,130,1,1,2,?,?,0 48,1,2,100,?,0,0,100,0,0,?,?,?,0 48,1,2,130,245,0,0,160,0,0,?,?,?,0 48,1,2,140,238,0,0,118,0,0,?,?,?,0 48,1,3,110,211,0,0,138,0,0,?,?,6,0 49,0,2,110,?,0,0,160,0,0,?,?,?,0 49,0,2,110,?,0,0,160,0,0,?,?,?,0 49,0,2,124,201,0,0,164,0,0,?,?,?,0 49,0,3,130,207,0,1,135,0,0,?,?,?,0 49,1,2,100,253,0,0,174,0,0,?,?,?,0 49,1,3,140,187,0,0,172,0,0,?,?,?,0 49,1,4,120,297,?,0,132,0,1,2,?,?,0 49,1,4,140,?,0,0,130,0,0,?,?,?,0 50,0,2,110,202,0,0,145,0,0,?,?,?,0 50,0,4,120,328,0,0,110,1,1,2,?,?,0 50,1,2,120,168,0,0,160,0,0,?,0,?,0 50,1,2,140,216,0,0,170,0,0,?,?,3,0 50,1,2,170,209,0,1,116,0,0,?,?,?,0 50,1,4,140,129,0,0,135,0,0,?,?,?,0 50,1,4,150,215,0,0,140,1,0,?,?,?,0 51,0,2,160,194,0,0,170,0,0,?,?,?,0 51,0,3,110,190,0,0,120,0,0,?,?,?,0 51,0,3,130,220,0,0,160,1,2,1,?,?,0 51,0,3,150,200,0,0,120,0,0.5,1,?,?,0 51,1,2,125,188,0,0,145,0,0,?,?,?,0 51,1,2,130,224,0,0,150,0,0,?,?,?,0 51,1,4,130,179,0,0,100,0,0,?,?,7,0 52,0,2,120,210,0,0,148,0,0,?,?,?,0 52,0,2,140,?,0,0,140,0,0,?,?,?,0 52,0,3,125,272,0,0,139,0,0,?,?,?,0 52,0,4,130,180,0,0,140,1,1.5,2,?,?,0 52,1,2,120,284,0,0,118,0,0,?,?,?,0 52,1,2,140,100,0,0,138,1,0,?,?,?,0 52,1,2,160,196,0,0,165,0,0,?,?,?,0 52,1,3,140,259,0,1,170,0,0,?,?,?,0 53,0,2,113,468,?,0,127,0,0,?,?,?,0 53,0,2,140,216,0,0,142,1,2,2,?,?,0 53,0,3,120,274,0,0,130,0,0,?,?,?,0 53,1,2,120,?,0,0,132,0,0,?,?,?,0 53,1,2,140,320,0,0,162,0,0,?,?,?,0 53,1,3,120,195,0,0,140,0,0,?,?,?,0 53,1,4,124,260,0,1,112,1,3,2,?,?,0 53,1,4,130,182,0,0,148,0,0,?,?,?,0 53,1,4,140,243,0,0,155,0,0,?,?,?,0 54,0,2,120,221,0,0,138,0,1,1,?,?,0 54,0,2,120,230,1,0,140,0,0,?,?,?,0 54,0,2,120,273,0,0,150,0,1.5,2,?,?,0 54,0,2,130,253,0,1,155,0,0,?,?,?,0 54,0,2,140,309,?,1,140,0,0,?,?,?,0 54,0,2,150,230,0,0,130,0,0,?,?,?,0 54,0,2,160,312,0,0,130,0,0,?,?,?,0 54,1,1,120,171,0,0,137,0,2,1,?,?,0 54,1,2,110,208,0,0,142,0,0,?,?,?,0 54,1,2,120,238,0,0,154,0,0,?,?,?,0 54,1,2,120,246,0,0,110,0,0,?,?,?,0 54,1,2,160,195,0,1,130,0,1,1,?,?,0 54,1,2,160,305,0,0,175,0,0,?,?,?,0 54,1,3,120,217,0,0,137,0,0,?,?,?,0 54,1,3,150,?,0,0,122,0,0,?,?,?,0 54,1,4,150,365,0,1,134,0,1,1,?,?,0 55,0,2,110,344,0,1,160,0,0,?,?,?,0 55,0,2,122,320,0,0,155,0,0,?,?,?,0 55,0,2,130,394,0,2,150,0,0,?,?,?,0 55,1,2,120,256,1,0,137,0,0,?,?,7,0 55,1,2,140,196,0,0,150,0,0,?,?,7,0 55,1,2,145,326,0,0,155,0,0,?,?,?,0 55,1,3,110,277,0,0,160,0,0,?,?,?,0 55,1,3,120,220,0,2,134,0,0,?,?,?,0 55,1,4,120,270,0,0,140,0,0,?,?,?,0 55,1,4,140,229,0,0,110,1,0.5,2,?,?,0 56,0,3,130,219,?,1,164,0,0,?,?,7,0 56,1,2,130,184,0,0,100,0,0,?,?,?,0 56,1,3,130,?,0,0,114,0,0,?,?,?,0 56,1,3,130,276,0,0,128,1,1,1,?,6,0 56,1,4,120,85,0,0,140,0,0,?,?,?,0 57,0,1,130,308,0,0,98,0,1,2,?,?,0 57,0,4,180,347,0,1,126,1,0.8,2,?,?,0 57,1,2,140,260,1,0,140,0,0,?,?,6,0 58,1,2,130,230,0,0,150,0,0,?,?,?,0 58,1,2,130,251,0,0,110,0,0,?,?,?,0 58,1,3,140,179,0,0,160,0,0,?,?,?,0 58,1,4,135,222,0,0,100,0,0,?,?,?,0 59,0,2,130,188,0,0,124,0,1,2,?,?,0 59,1,2,140,287,0,0,150,0,0,?,?,?,0 59,1,3,130,318,0,0,120,1,1,2,?,3,0 59,1,3,180,213,0,0,100,0,0,?,?,?,0 59,1,4,140,?,0,0,140,0,0,?,0,?,0 60,1,3,120,246,0,2,135,0,0,?,?,?,0 61,0,4,130,294,0,1,120,1,1,2,?,?,0 61,1,4,125,292,0,1,115,1,0,?,?,?,0 62,0,1,160,193,0,0,116,0,0,?,?,?,0 62,1,2,140,271,0,0,152,0,1,1,?,?,0 31,1,4,120,270,0,0,153,1,1.5,2,?,?,1 33,0,4,100,246,0,0,150,1,1,2,?,?,1 34,1,1,140,156,0,0,180,0,0,?,?,?,1 35,1,2,110,257,0,0,140,0,0,?,?,?,1 36,1,2,120,267,0,0,160,0,3,2,?,?,1 37,1,4,140,207,0,0,130,1,1.5,2,?,?,1 38,1,4,110,196,0,0,166,0,0,?,?,?,1 38,1,4,120,282,0,0,170,0,0,?,?,?,1 38,1,4,92,117,0,0,134,1,2.5,2,?,?,1 40,1,4,120,466,?,0,152,1,1,2,?,6,1 41,1,4,110,289,0,0,170,0,0,?,?,6,1 41,1,4,120,237,?,0,138,1,1,2,?,?,1 43,1,4,150,247,0,0,130,1,2,2,?,?,1 46,1,4,110,202,0,0,150,1,0,?,?,?,1 46,1,4,118,186,0,0,124,0,0,?,?,7,1 46,1,4,120,277,0,0,125,1,1,2,?,?,1 47,1,3,140,193,0,0,145,1,1,2,?,?,1 47,1,4,150,226,0,0,98,1,1.5,2,0,7,1 48,1,4,106,263,1,0,110,0,0,?,?,?,1 48,1,4,120,260,0,0,115,0,2,2,?,?,1 48,1,4,160,268,0,0,103,1,1,2,?,?,1 49,0,3,160,180,0,0,156,0,1,2,?,?,1 49,1,3,115,265,0,0,175,0,0,?,?,?,1 49,1,4,130,206,0,0,170,0,0,?,?,?,1 50,0,3,140,288,0,0,140,1,0,?,?,7,1 50,1,4,145,264,0,0,150,0,0,?,?,?,1 51,0,4,160,303,0,0,150,1,1,2,?,?,1 52,1,4,130,225,0,0,120,1,2,2,?,?,1 54,1,4,125,216,0,0,140,0,0,?,?,?,1 54,1,4,125,224,0,0,122,0,2,2,?,?,1 55,1,4,140,201,0,0,130,1,3,2,?,?,1 57,1,2,140,265,0,1,145,1,1,2,?,?,1 58,1,3,130,213,0,1,140,0,0,?,?,6,1 59,0,4,130,338,1,1,130,1,1.5,2,?,?,1 60,1,4,100,248,0,0,125,0,1,2,?,?,1 63,1,4,150,223,0,0,115,0,0,?,?,?,1 65,1,4,140,306,1,0,87,1,1.5,2,?,?,1 32,1,4,118,529,0,0,130,0,0,?,?,?,1 38,1,4,110,?,0,0,150,1,1,2,?,?,1 39,1,4,110,280,0,0,150,0,0,?,?,6,1 40,0,4,150,392,0,0,130,0,2,2,?,6,1 43,1,1,120,291,0,1,155,0,0,?,?,?,1 45,1,4,130,219,0,1,130,1,1,2,?,?,1 46,1,4,120,231,0,0,115,1,0,?,?,?,1 46,1,4,130,222,0,0,112,0,0,?,?,?,1 48,1,4,122,275,1,1,150,1,2,3,?,?,1 48,1,4,160,193,0,0,102,1,3,2,?,?,1 48,1,4,160,329,0,0,92,1,1.5,2,?,?,1 48,1,4,160,355,0,0,99,1,2,2,?,?,1 50,1,4,130,233,0,0,121,1,2,2,?,7,1 52,1,4,120,182,0,0,150,0,0,?,?,?,1 52,1,4,170,?,0,0,126,1,1.5,2,?,?,1 53,1,4,120,246,0,0,116,1,0,?,?,?,1 54,1,3,120,237,0,0,150,1,1.5,?,?,7,1 54,1,4,130,242,0,0,91,1,1,2,?,?,1 54,1,4,130,603,1,0,125,1,1,2,?,?,1 54,1,4,140,?,0,0,118,1,0,?,?,?,1 54,1,4,200,198,0,0,142,1,2,2,?,?,1 55,1,4,140,268,0,0,128,1,1.5,2,?,?,1 56,1,4,150,213,1,0,125,1,1,2,?,?,1 57,1,4,150,255,0,0,92,1,3,2,?,?,1 58,1,3,160,211,1,1,92,0,0,?,?,?,1 58,1,4,130,263,0,0,140,1,2,2,?,?,1 41,1,4,130,172,0,1,130,0,2,2,?,?,1 43,1,4,120,175,0,0,120,1,1,2,?,7,1 44,1,2,150,288,0,0,150,1,3,2,?,?,1 44,1,4,130,290,0,0,100,1,2,2,?,?,1 46,1,1,140,272,1,0,175,0,2,2,?,?,1 47,0,3,135,248,1,0,170,0,0,?,?,?,1 48,0,4,138,214,0,0,108,1,1.5,2,?,?,1 49,1,4,130,341,0,0,120,1,1,2,?,?,1 49,1,4,140,234,0,0,140,1,1,2,?,?,1 51,1,3,135,160,0,0,150,0,2,2,?,?,1 52,1,4,112,342,0,1,96,1,1,2,?,?,1 52,1,4,130,298,0,0,110,1,1,2,?,?,1 52,1,4,140,404,0,0,124,1,2,2,?,?,1 52,1,4,160,246,0,1,82,1,4,2,?,?,1 53,1,3,145,518,0,0,130,0,0,?,?,?,1 53,1,4,180,285,0,1,120,1,1.5,2,?,?,1 54,1,4,140,216,0,0,105,0,1.5,2,?,?,1 55,1,1,140,295,0,?,136,0,0,?,?,?,1 55,1,2,160,292,1,0,143,1,2,2,?,?,1 55,1,4,145,248,0,0,96,1,2,2,?,?,1 56,0,2,120,279,0,0,150,0,1,2,?,?,1 56,1,4,150,230,0,1,124,1,1.5,2,?,?,1 56,1,4,170,388,0,1,122,1,2,2,?,?,1 58,1,2,136,164,0,1,99,1,2,2,?,?,1 59,1,4,130,?,0,0,125,0,0,?,?,?,1 59,1,4,140,264,1,2,119,1,0,?,?,?,1 65,1,4,170,263,1,0,112,1,2,2,?,?,1 66,1,4,140,?,0,0,94,1,1,2,?,?,1 41,1,4,120,336,0,0,118,1,3,2,?,?,1 43,1,4,140,288,0,0,135,1,2,2,?,?,1 44,1,4,135,491,0,0,135,0,0,?,?,?,1 47,0,4,120,205,0,0,98,1,2,2,?,6,1 47,1,4,160,291,0,1,158,1,3,2,?,?,1 49,1,4,128,212,0,0,96,1,0,?,?,?,1 49,1,4,150,222,0,0,122,0,2,2,?,?,1 50,1,4,140,231,0,1,140,1,5,2,?,?,1 50,1,4,140,341,0,1,125,1,2.5,2,?,?,1 52,1,4,140,266,0,0,134,1,2,2,?,?,1 52,1,4,160,331,0,0,94,1,2.5,?,?,?,1 54,0,3,130,294,0,1,100,1,0,2,?,?,1 56,1,4,155,342,1,0,150,1,3,2,?,?,1 58,0,2,180,393,0,0,110,1,1,2,?,7,1 65,1,4,130,275,0,1,115,1,1,2,?,?,1

Data Files/labor-neg-nominal-v2.arff

%Date: Tue, 15 Nov 88 15:44:08 EST %From: stan <[email protected]> %Message-Id: <[email protected]> %To: [email protected] % %1. Title: Final settlements in labor negotitions in Canadian industry % %2. Source Information % -- Creators: Collective Barganing Review, montly publication, % Labour Canada, Industrial Relations Information Service, % Ottawa, Ontario, K1A 0J2, Canada, (819) 997-3117 % The data includes all collective agreements reached % in the business and personal services sector for locals % with at least 500 members (teachers, nurses, university % staff, police, etc) in Canada in 87 and first quarter of 88. % -- Donor: Stan Matwin, Computer Science Dept, University of Ottawa, % 34 Somerset East, K1N 9B4, ([email protected]) % -- Date: November 1988 % %3. Past Usage: % -- testing concept learning software, in particular % an experimental method to learn two-tiered concept descriptions. % The data was used to learn the description of an acceptable % and unacceptable contract. % The unacceptable contracts were either obtained by interviewing % experts, or by inventing near misses. % Examples of use are described in: % Bergadano, F., Matwin, S., Michalski, R., % Zhang, J., Measuring Quality of Concept Descriptions, % Procs. of the 3rd European Working Sessions on Learning, % Glasgow, October 1988. % Bergadano, F., Matwin, S., Michalski, R., Zhang, J., % Representing and Acquiring Imprecise and Context-dependent % Concepts in Knowledge-based Systems, Procs. of ISMIS'88, % North Holland, 1988. %4. Relevant Information: % -- data was used to test 2tier approach with learning %from positive and negative examples % %5. Number of Instances: 57 % %6. Number of Attributes: 16 % %7. Attribute Information: % 1. dur: duration of agreement % [1..7] % 2 wage1.wage : wage increase in first year of contract % [2.0 .. 7.0] % 3 wage2.wage : wage increase in second year of contract % [2.0 .. 7.0] % 4 wage3.wage : wage increase in third year of contract % [2.0 .. 7.0] % 5 cola : cost of living allowance % [none, tcf, tc] % 6 hours.hrs : number of working hours during week % [35 .. 40] % 7 pension : employer contributions to pension plan % [none, ret_allw, empl_contr] % 8 stby_pay : standby pay % [2 .. 25] % 9 shift_diff : shift differencial : supplement for work on II and III shift % [1 .. 25] % 10 educ_allw.boolean : education allowance % [yes no] % 11 holidays : number of statutory holidays % [9 .. 15] % 12 vacation : number of paid vacation days % [ba, avg, gnr] % 13 lngtrm_disabil.boolean : % employer's help during employee longterm disabil % ity [yes , no] % 14 dntl_ins : employers contribution towards the dental plan % [none, half, full] % 15 bereavement.boolean : employer's financial contribution towards the % covering the costs of bereavement % [yes , no] % 16 empl_hplan : employer's contribution towards the health plan % [none, half, full] % %8. Missing Attribute Values: None % %9. Class Distribution: % %10. Exceptions from format instructions: no commas between attribute values. % %--------------------------------------------------------------------------- @relation labor-neg-nominal % Classes % ------- % good, bad. % Attributes % ---------- @attribute duration { 1, 2, 3 ,unknown } @attribute "wage increase first year" { low, medium, high ,unknown } @attribute "wage increase second year" { low, medium, high ,unknown } @attribute "wage increase third year" { low, medium, high ,unknown } @attribute "cost of living adjustment"{ none, tcf, tc ,unknown } @attribute "working hours" { sub35, sub40, equal40 ,unknown } @attribute pension { none, ret_allw, empl_contr ,unknown } @attribute "standby pay" { 2, 4, 8, 12, 13 ,unknown } @attribute "shift differential" { 0, 1, 2, 3, 4, 5,6, 10, 11, 25 ,unknown } @attribute "education allowance" {yes, no ,unknown } @attribute "statutory holidays" { 9, 10, 11, 12,13,15 ,unknown } @attribute vacation { "below average", average, generous ,unknown } @attribute "longterm disability assistance" { yes, no ,unknown } @attribute "contribution to dental plan" { none, half, full ,unknown } @attribute "bereavement assistance" { yes, no ,unknown } @attribute "contribution to health plan" { none, half, full ,unknown } @attribute class { good, bad } @data 1,medium,unknown,unknown,unknown,equal40,unknown,unknown,2,unknown,11,average,unknown,unknown,yes,unknown,good 2,medium,medium,unknown,unknown,sub40,ret_allw,unknown,unknown,yes,11,"below average",unknown,full,unknown,full,good unknown,unknown,unknown,unknown,unknown,sub40,empl_contr,unknown,5,unknown,11,generous,yes,half,yes,half,good 3,medium,medium,medium,tc,unknown,unknown,unknown,unknown,yes,unknown,unknown,unknown,unknown,yes,unknown,good 3,medium,,medium,medium,unknown, equal40,unknown,unknown,unknown,unknown,12,average,unknown,half,yes,half,good 2,low,low,unknown,unknown,sub35,unknown,unknown,6,yes,12,average,unknown,unknown,unknown,unknown,good 3,medium,medium,medium,tc,unknown,empl_contr,unknown,unknown,unknown,12,generous,yes,none,yes,half,good 3,high,medium,low,unknown,equal40,unknown,unknown,3,unknown,12,"below average",unknown,unknown,unknown,unknown,good 2,low,high,unknown,unknown,sub40,unknown,12,25,yes,11,"below average",yes,half,yes,unknown,good 1,medium,unknown,unknown,none,equal40,empl_contr,unknown,4,unknown,11,generous,yes,full,unknown,unknown,good 3,medium,medium,medium,none,sub40,unknown,unknown,3,unknown,13,generous,unknown,unknown,yes,full,good 2,high,high,unknown,unknown,sub40,unknown,unknown,4,unknown,15,unknown,unknown,full,unknown,unknown,good 2,medium,medium,unknown,none,equal40,unknown,unknown,2,no,10,"below average",no,half,unknown,half,bad 3,medium,medium,medium,tcf,sub40,unknown,unknown,4,unknown,13,generous,unknown,full,yes,full,good 1,low,unknown,unknown,none,sub40,unknown,unknown,10,no,11,generous,unknown,unknown,unknown,unknown,good 2,medium,medium,unknown,none,sub40,empl_contr,unknown,unknown,unknown,11,average,unknown,full,yes,unknown,good 1,low,unknown,unknown,unknown,sub35,unknown,unknown,2,unknown,12,"below average",unknown,unknown,unknown,unknown,good 1,low,unknown,unknown,tc,equal40,ret_allw,2,3,no,9,"below average",yes,half,unknown,none,bad 1,low,unknown,unknown,none,sub40,none,unknown,unknown,yes,11,average,no,none,no,none,bad 2,medium,medium,unknown,tcf,sub35,unknown,13,5,unknown,15,generous,unknown,unknown,unknown,unknown,good 2,medium,medium,unknown,unknown,sub40,unknown,unknown,4,unknown,12,generous,unknown,full,unknown,full,good 2,low,low,unknown,unknown,equal40,none,unknown,unknown,unknown,11,"below average",unknown,unknown,unknown,unknown,bad 3,medium,medium,medium,tcf,sub35,unknown,unknown,unknown,unknown,unknown,unknown,unknown,unknown,unknown,unknown,good 2,medium,medium,unknown,unknown,equal40,unknown,unknown,4,unknown,10,generous,unknown,half,unknown,full,good 1,high,unknown,unknown,unknown,sub40,unknown,8,3,unknown,9,generous,unknown,unknown,unknown,unknown,good 3,low,low,low,none,equal40,none,unknown,unknown,unknown,10,"below average",unknown,half,yes,full,bad 2,medium,medium,unknown,tcf,unknown,unknown,unknown,unknown,yes,10,"below average",yes,none,unknown,half,good 2,low,low,unknown,none,sub35,unknown,unknown,unknown,yes,12,generous,unknown,unknown,yes,full,good 2,medium,medium,unknown,none,sub40,unknown,unknown,5,no,11,"below average",yes,full,yes,full,good 3,low,low,unknown,unknown,sub35,none,unknown,unknown,unknown,10,average,unknown,unknown,yes,full,bad 3,medium,medium,medium,none,equal40,unknown,unknown,unknown,no,11,average,unknown,half,unknown,unknown,good 3,low,low,low,tc,equal40,none,unknown,5,no,10,"below average",yes,half,yes,full,bad 2,low,low,unknown,unknown,sub40,empl_contr,unknown,unknown,unknown,10,average,unknown,unknown,unknown,unknown,bad 2,medium,medium,unknown,none,equal40,none,unknown,3,no,10,"below average",no,none,unknown,none,bad 3,low,low,low,tc,equal40,none,2,1,no,10,"below average",no,half,yes,full,bad 2,low,low,unknown,none,equal40,none,unknown,unknown,no,11,average,yes,none,yes,full,bad 1,low,unknown,unknown,tc,equal40,ret_allw,4,0,no,11,generous,no,none,no,none,bad 1,low,unknown,unknown,none,sub40,empl_contr,2,3,no,9,"below average",yes,half,unknown,none,bad 3,low,low,low,unknown,sub40,empl_contr,unknown,unknown,unknown,10,average,unknown,unknown,yes,none,bad 2,medium,medium,unknown,none,equal40,unknown,unknown,4,unknown,12,average,yes,full,yes,half,good

Data Files/titanic.arff

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Data Files/zoo.arff

% Changes to WEKA Format: SRG - November 1994 % 1. Boolean attributes changed from 1 and 0 to Enumerated attribute with % values {true and false} % 2. Class Number (Attribute 18) changed to an Enumerated type with % values {1,2,3,4,5,6,7} % % 1. Title: Zoo database % % 2. Source Information % -- Creator: Richard Forsyth % -- Donor: Richard S. Forsyth % 8 Grosvenor Avenue % Mapperley Park % Nottingham NG3 5DX % 0602-621676 % -- Date: 5/15/1990 % % 3. Past Usage: % -- None known other than what is shown in Forsyth's PC/BEAGLE User's Guide. % % 4. Relevant Information: % -- A simple database containing 17 Boolean-valued attributes. The "type" % attribute appears to be the class attribute. Here is a breakdown of % which animals are in which type: (I find it unusual that there are % 2 instances of "frog" and one of "girl"!) % % Class# Set of animals: % ====== =============================================================== % 1 (41) aardvark, antelope, bear, boar, buffalo, calf, % cavy, cheetah, deer, dolphin, elephant, % fruitbat, giraffe, girl, goat, gorilla, hamster, % hare, leopard, lion, lynx, mink, mole, mongoose, % opossum, oryx, platypus, polecat, pony, % porpoise, puma, pussycat, raccoon, reindeer, % seal, sealion, squirrel, vampire, vole, wallaby,wolf % 2 (20) chicken, crow, dove, duck, flamingo, gull, hawk, % kiwi, lark, ostrich, parakeet, penguin, pheasant, % rhea, skimmer, skua, sparrow, swan, vulture, wren % 3 (5) pitviper, seasnake, slowworm, tortoise, tuatara % 4 (13) bass, carp, catfish, chub, dogfish, haddock, % herring, pike, piranha, seahorse, sole, stingray, tuna % 5 (4) frog, frog, newt, toad % 6 (8) flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp % 7 (10) clam, crab, crayfish, lobster, octopus, % scorpion, seawasp, slug, starfish, worm % % 5. Number of Instances: 101 % 6. Number of Attributes: 18 (animal name, 15 Boolean attributes, 2 numerics) % 7. Attribute Information: (name of attribute and type of value domain) % 1. animal name: Unique for each instance % 2. hair Boolean % 3. feathers Boolean % 4. eggs Boolean % 5. milk Boolean % 6. airborne Boolean % 7. aquatic Boolean % 8. predator Boolean % 9. toothed Boolean % 10. backbone Boolean % 11. breathes Boolean % 12. venomous Boolean % 13. fins Boolean % 14. legs Numeric (set of values: {0,2,4,5,6,8}) % 15. tail Boolean % 16. domestic Boolean % 17. catsize Boolean % 18. type Numeric (integer values in range [1,7]) % % 8. Missing Attribute Values: None % 9. Class Distribution: Given above @RELATION zoo @ATTRIBUTE animal {aardvark,antelope,bass,bear,boar,buffalo,calf,carp,catfish,cavy,cheetah,chicken,chub,clam,crab,crayfish,crow,deer,dogfish,dolphin,dove,duck,elephant,flamingo,flea,frog,fruitbat,giraffe,girl,gnat,goat,gorilla,gull,haddock,hamster,hare,hawk,herring,honeybee,housefly,kiwi,ladybird,lark,leopard,lion,lobster,lynx,mink,mole,mongoose,moth,newt,octopus,opossum,oryx,ostrich,parakeet,penguin,pheasant,pike,piranha,pitviper,platypus,polecat,pony,porpoise,puma,pussycat,raccoon,reindeer,rhea,scorpion,seahorse,seal,sealion,seasnake,seawasp,skimmer,skua,slowworm,slug,sole,sparrow,squirrel,starfish,stingray,swan,termite,toad,tortoise,tuatara,tuna,vampire,vole,vulture,wallaby,wasp,wolf,worm,wren} @ATTRIBUTE hair {false, true} @ATTRIBUTE feathers {false, true} @ATTRIBUTE eggs {false, true} @ATTRIBUTE milk {false, true} @ATTRIBUTE airborne {false, true} @ATTRIBUTE aquatic {false, true} @ATTRIBUTE predator {false, true} @ATTRIBUTE toothed {false, true} @ATTRIBUTE backbone {false, true} @ATTRIBUTE breathes {false, true} @ATTRIBUTE venomous {false, true} @ATTRIBUTE fins {false, true} % hakank: changed this since it's simpler for e.g. Apriori % @ATTRIBUTE legs INTEGER [0,9] @ATTRIBUTE legs {0,1,2,3,4,5,6,7,8,9} @ATTRIBUTE tail {false, true} @ATTRIBUTE domestic {false, true} @ATTRIBUTE catsize {false, true} @ATTRIBUTE type { 1,2,3,4,5,6,7 } @DATA % % Instances (101): % aardvark,true,false,false,true,false,false,true,true,true,true,false,false,4,false,false,true,1 antelope,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 bass,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,false,4 bear,true,false,false,true,false,false,true,true,true,true,false,false,4,false,false,true,1 boar,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 buffalo,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 calf,true,false,false,true,false,false,false,true,true,true,false,false,4,true,true,true,1 carp,false,false,true,false,false,true,false,true,true,false,false,true,0,true,true,false,4 catfish,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,false,4 cavy,true,false,false,true,false,false,false,true,true,true,false,false,4,false,true,false,1 cheetah,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 chicken,false,true,true,false,true,false,false,false,true,true,false,false,2,true,true,false,2 chub,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,false,4 clam,false,false,true,false,false,false,true,false,false,false,false,false,0,false,false,false,7 crab,false,false,true,false,false,true,true,false,false,false,false,false,4,false,false,false,7 crayfish,false,false,true,false,false,true,true,false,false,false,false,false,6,false,false,false,7 crow,false,true,true,false,true,false,true,false,true,true,false,false,2,true,false,false,2 deer,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 dogfish,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,true,4 dolphin,false,false,false,true,false,true,true,true,true,true,false,true,0,true,false,true,1 dove,false,true,true,false,true,false,false,false,true,true,false,false,2,true,true,false,2 duck,false,true,true,false,true,true,false,false,true,true,false,false,2,true,false,false,2 elephant,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 flamingo,false,true,true,false,true,false,false,false,true,true,false,false,2,true,false,true,2 flea,false,false,true,false,false,false,false,false,false,true,false,false,6,false,false,false,6 frog,false,false,true,false,false,true,true,true,true,true,false,false,4,false,false,false,5 frog,false,false,true,false,false,true,true,true,true,true,true,false,4,false,false,false,5 fruitbat,true,false,false,true,true,false,false,true,true,true,false,false,2,true,false,false,1 giraffe,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 girl,true,false,false,true,false,false,true,true,true,true,false,false,2,false,true,true,1 gnat,false,false,true,false,true,false,false,false,false,true,false,false,6,false,false,false,6 goat,true,false,false,true,false,false,false,true,true,true,false,false,4,true,true,true,1 gorilla,true,false,false,true,false,false,false,true,true,true,false,false,2,false,false,true,1 gull,false,true,true,false,true,true,true,false,true,true,false,false,2,true,false,false,2 haddock,false,false,true,false,false,true,false,true,true,false,false,true,0,true,false,false,4 hamster,true,false,false,true,false,false,false,true,true,true,false,false,4,true,true,false,1 hare,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,false,1 hawk,false,true,true,false,true,false,true,false,true,true,false,false,2,true,false,false,2 herring,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,false,4 honeybee,true,false,true,false,true,false,false,false,false,true,true,false,6,false,true,false,6 housefly,true,false,true,false,true,false,false,false,false,true,false,false,6,false,false,false,6 kiwi,false,true,true,false,false,false,true,false,true,true,false,false,2,true,false,false,2 ladybird,false,false,true,false,true,false,true,false,false,true,false,false,6,false,false,false,6 lark,false,true,true,false,true,false,false,false,true,true,false,false,2,true,false,false,2 leopard,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 lion,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 lobster,false,false,true,false,false,true,true,false,false,false,false,false,6,false,false,false,7 lynx,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 mink,true,false,false,true,false,true,true,true,true,true,false,false,4,true,false,true,1 mole,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,false,1 mongoose,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 moth,true,false,true,false,true,false,false,false,false,true,false,false,6,false,false,false,6 newt,false,false,true,false,false,true,true,true,true,true,false,false,4,true,false,false,5 octopus,false,false,true,false,false,true,true,false,false,false,false,false,8,false,false,true,7 opossum,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,false,1 oryx,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,true,1 ostrich,false,true,true,false,false,false,false,false,true,true,false,false,2,true,false,true,2 parakeet,false,true,true,false,true,false,false,false,true,true,false,false,2,true,true,false,2 penguin,false,true,true,false,false,true,true,false,true,true,false,false,2,true,false,true,2 pheasant,false,true,true,false,true,false,false,false,true,true,false,false,2,true,false,false,2 pike,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,true,4 piranha,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,false,4 pitviper,false,false,true,false,false,false,true,true,true,true,true,false,0,true,false,false,3 platypus,true,false,true,true,false,true,true,false,true,true,false,false,4,true,false,true,1 polecat,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 pony,true,false,false,true,false,false,false,true,true,true,false,false,4,true,true,true,1 porpoise,false,false,false,true,false,true,true,true,true,true,false,true,0,true,false,true,1 puma,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 pussycat,true,false,false,true,false,false,true,true,true,true,false,false,4,true,true,true,1 raccoon,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 reindeer,true,false,false,true,false,false,false,true,true,true,false,false,4,true,true,true,1 rhea,false,true,true,false,false,false,true,false,true,true,false,false,2,true,false,true,2 scorpion,false,false,false,false,false,false,true,false,false,true,true,false,8,true,false,false,7 seahorse,false,false,true,false,false,true,false,true,true,false,false,true,0,true,false,false,4 seal,true,false,false,true,false,true,true,true,true,true,false,true,0,false,false,true,1 sealion,true,false,false,true,false,true,true,true,true,true,false,true,2,true,false,true,1 seasnake,false,false,false,false,false,true,true,true,true,false,true,false,0,true,false,false,3 seawasp,false,false,true,false,false,true,true,false,false,false,true,false,0,false,false,false,7 skimmer,false,true,true,false,true,true,true,false,true,true,false,false,2,true,false,false,2 skua,false,true,true,false,true,true,true,false,true,true,false,false,2,true,false,false,2 slowworm,false,false,true,false,false,false,true,true,true,true,false,false,0,true,false,false,3 slug,false,false,true,false,false,false,false,false,false,true,false,false,0,false,false,false,7 sole,false,false,true,false,false,true,false,true,true,false,false,true,0,true,false,false,4 sparrow,false,true,true,false,true,false,false,false,true,true,false,false,2,true,false,false,2 squirrel,true,false,false,true,false,false,false,true,true,true,false,false,2,true,false,false,1 starfish,false,false,true,false,false,true,true,false,false,false,false,false,5,false,false,false,7 stingray,false,false,true,false,false,true,true,true,true,false,true,true,0,true,false,true,4 swan,false,true,true,false,true,true,false,false,true,true,false,false,2,true,false,true,2 termite,false,false,true,false,false,false,false,false,false,true,false,false,6,false,false,false,6 toad,false,false,true,false,false,true,false,true,true,true,false,false,4,false,false,false,5 tortoise,false,false,true,false,false,false,false,false,true,true,false,false,4,true,false,true,3 tuatara,false,false,true,false,false,false,true,true,true,true,false,false,4,true,false,false,3 tuna,false,false,true,false,false,true,true,true,true,false,false,true,0,true,false,true,4 vampire,true,false,false,true,true,false,false,true,true,true,false,false,2,true,false,false,1 vole,true,false,false,true,false,false,false,true,true,true,false,false,4,true,false,false,1 vulture,false,true,true,false,true,false,true,false,true,true,false,false,2,true,false,true,2 wallaby,true,false,false,true,false,false,false,true,true,true,false,false,2,true,false,true,1 wasp,true,false,true,false,true,false,false,false,false,true,true,false,6,false,false,false,6 wolf,true,false,false,true,false,false,true,true,true,true,false,false,4,true,false,true,1 worm,false,false,true,false,false,false,false,false,false,true,false,false,0,false,false,false,7 wren,false,true,true,false,true,false,false,false,true,true,false,false,2,true,false,false,2

Data Files/zoo2_x.arff

@relation zoo_filtered-weka.filters.unsupervised.instance.NonSparseToSparse @attribute hair {false,true} @attribute feathers {false,true} @attribute eggs {false,true} @attribute milk {false,true} @attribute airborne {false,true} @attribute aquatic {false,true} @attribute predator {false,true} @attribute toothed {false,true} @attribute backbone {false,true} @attribute breathes {false,true} @attribute venomous {false,true} @attribute fins {false,true} @attribute legs {0,1,2,3,4,5,6,7,8,9} @attribute tail {false,true} @attribute domestic {false,true} @attribute catsize {false,true} @attribute type {1,2,3,4,5,6,7} @data {0 true,3 true,6 true,7 true,8 true,9 true,12 4,15 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,16 4} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,14 true,15 true} {2 true,5 true,7 true,8 true,11 true,13 true,14 true,16 4} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,16 4} {0 true,3 true,7 true,8 true,9 true,12 4,14 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,14 true,16 2} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,16 4} {2 true,6 true,16 7} {2 true,5 true,6 true,12 4,16 7} {2 true,5 true,6 true,12 6,16 7} {1 true,2 true,4 true,6 true,8 true,9 true,12 2,13 true,16 2} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,15 true,16 4} {3 true,5 true,6 true,7 true,8 true,9 true,11 true,13 true,15 true} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,14 true,16 2} {1 true,2 true,4 true,5 true,8 true,9 true,12 2,13 true,16 2} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,15 true,16 2} {2 true,9 true,12 6,16 6} {2 true,5 true,6 true,7 true,8 true,9 true,12 4,16 5} {2 true,5 true,6 true,7 true,8 true,9 true,10 true,12 4,16 5} {0 true,3 true,4 true,7 true,8 true,9 true,12 2,13 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 2,14 true,15 true} {2 true,4 true,9 true,12 6,16 6} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,14 true,15 true} {0 true,3 true,7 true,8 true,9 true,12 2,15 true} {1 true,2 true,4 true,5 true,6 true,8 true,9 true,12 2,13 true,16 2} {2 true,5 true,7 true,8 true,11 true,13 true,16 4} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,14 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true} {1 true,2 true,4 true,6 true,8 true,9 true,12 2,13 true,16 2} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,16 4} {0 true,2 true,4 true,9 true,10 true,12 6,14 true,16 6} {0 true,2 true,4 true,9 true,12 6,16 6} {1 true,2 true,6 true,8 true,9 true,12 2,13 true,16 2} {2 true,4 true,6 true,9 true,12 6,16 6} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,16 2} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {2 true,5 true,6 true,12 6,16 7} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,5 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,2 true,4 true,9 true,12 6,16 6} {2 true,5 true,6 true,7 true,8 true,9 true,12 4,13 true,16 5} {2 true,5 true,6 true,12 8,15 true,16 7} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,15 true} {1 true,2 true,8 true,9 true,12 2,13 true,15 true,16 2} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,14 true,16 2} {1 true,2 true,5 true,6 true,8 true,9 true,12 2,13 true,15 true,16 2} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,16 2} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,15 true,16 4} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,16 4} {2 true,6 true,7 true,8 true,9 true,10 true,13 true,16 3} {0 true,2 true,3 true,5 true,6 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,14 true,15 true} {3 true,5 true,6 true,7 true,8 true,9 true,11 true,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,14 true,15 true} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true,14 true,15 true} {1 true,2 true,6 true,8 true,9 true,12 2,13 true,15 true,16 2} {6 true,9 true,10 true,12 8,13 true,16 7} {2 true,5 true,7 true,8 true,11 true,13 true,16 4} {0 true,3 true,5 true,6 true,7 true,8 true,9 true,11 true,15 true} {0 true,3 true,5 true,6 true,7 true,8 true,9 true,11 true,12 2,13 true,15 true} {5 true,6 true,7 true,8 true,10 true,13 true,16 3} {2 true,5 true,6 true,10 true,16 7} {1 true,2 true,4 true,5 true,6 true,8 true,9 true,12 2,13 true,16 2} {1 true,2 true,4 true,5 true,6 true,8 true,9 true,12 2,13 true,16 2} {2 true,6 true,7 true,8 true,9 true,13 true,16 3} {2 true,9 true,16 7} {2 true,5 true,7 true,8 true,11 true,13 true,16 4} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,16 2} {0 true,3 true,7 true,8 true,9 true,12 2,13 true} {2 true,5 true,6 true,12 5,16 7} {2 true,5 true,6 true,7 true,8 true,10 true,11 true,13 true,15 true,16 4} {1 true,2 true,4 true,5 true,8 true,9 true,12 2,13 true,15 true,16 2} {2 true,9 true,12 6,16 6} {2 true,5 true,7 true,8 true,9 true,12 4,16 5} {2 true,8 true,9 true,12 4,13 true,15 true,16 3} {2 true,6 true,7 true,8 true,9 true,12 4,13 true,16 3} {2 true,5 true,6 true,7 true,8 true,11 true,13 true,15 true,16 4} {0 true,3 true,4 true,7 true,8 true,9 true,12 2,13 true} {0 true,3 true,7 true,8 true,9 true,12 4,13 true} {1 true,2 true,4 true,6 true,8 true,9 true,12 2,13 true,15 true,16 2} {0 true,3 true,7 true,8 true,9 true,12 2,13 true,15 true} {0 true,2 true,4 true,9 true,10 true,12 6,16 6} {0 true,3 true,6 true,7 true,8 true,9 true,12 4,13 true,15 true} {2 true,9 true,16 7} {1 true,2 true,4 true,8 true,9 true,12 2,13 true,16 2}

Data_Files_Arff/Data Files/autoPrice.arff

Data_Files_Arff/Data Files/baskball.arff

Data_Files_Arff/Data Files/bolts.arff

Data_Files_Arff/Data Files/cloud.arff

Data_Files_Arff/Data Files/hungarian.arff

Data_Files_Arff/Data Files/labor-neg-nominal-v2.arff

Data_Files_Arff/Data Files/titanic.arff

Data_Files_Arff/Data Files/zoo.arff

Data_Files_Arff/Data Files/zoo2_x.arff

Data_Files_Arff/exper1.arff

Data_Files_Arff/golf.arff

@RELATION golf @ATTRIBUTE outlook {sunny,overcast, rain} @ATTRIBUTE temperature integer @ATTRIBUTE humidity integer @ATTRIBUTE windy {false, true} @ATTRIBUTE class {dont_play, play} @DATA sunny, 65, 85, false, dont_play sunny, 80, 90, true, dont_play overcast, 83, 78, false, play rain, 70, 96, false, play rain, 68, 80, false, play rain, 65, 70, true, play

Data_Files_Arff/iris_discretized.arff

@relation iris-weka.filters.DiscretizeFilter-O-Rfirst-last

@attribute sepallength {'\'(-inf-5.5]\''	'\'(5.5-6.1]\''	'\'(6.1-inf)\''}
@attribute sepalwidth {'\'(-inf-2.9]\''	'\'(2.9-3.3]\''	'\'(3.3-inf)\''}
@attribute petallength {'\'(-inf-1.9]\''	'\'(1.9-4.7]\''	'\'(4.7-inf)\''}
@attribute petalwidth {'\'(-inf-0.6]\''	'\'(0.6-1.7]\''	'\'(1.7-inf)\''}
@attribute class {Iris-setosa	Iris-versicolor	Iris-virginica}

@data

'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(5.5-6.1]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(5.5-6.1]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(5.5-6.1]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(3.3-inf)\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(-inf-1.9]\''	'\'(-inf-0.6]\''	Iris-setosa
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(3.3-inf)\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-versicolor
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(-inf-5.5]\''	'\'(-inf-2.9]\''	'\'(1.9-4.7]\''	'\'(0.6-1.7]\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(3.3-inf)\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(3.3-inf)\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(3.3-inf)\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(0.6-1.7]\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(3.3-inf)\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(-inf-2.9]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(6.1-inf)\''	'\'(3.3-inf)\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica
'\'(5.5-6.1]\''	'\'(2.9-3.3]\''	'\'(4.7-inf)\''	'\'(1.7-inf)\''	Iris-virginica

Data_Files_Arff/labor_discretized.arff

@relation labor-neg-data-weka.filters.DiscretizeFilter-B10-Rfirst-last

@attribute duration {'\'(-inf-1.2]\''

'\'(1.2-1.4]\''

'\'(1.4-1.6]\''

'\'(1.6-1.8]\''

'\'(1.8-2]\''

'\'(2-2.2]\''

'\'(2.2-2.4]\''

'\'(2.4-2.6]\''

'\'(2.6-2.8]\''

'\'(2.8-inf)\''}

@attribute wage-increase-first-year {'\'(-inf-2.5]\''

'\'(2.5-3]\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4-4.5]\''

'\'(4.5-5]\''

'\'(5-5.5]\''

'\'(5.5-6]\''

'\'(6-6.5]\''

'\'(6.5-inf)\''}

@attribute wage-increase-second-year {'\'(-inf-2.5]\''

'\'(2.5-3]\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4-4.5]\''

'\'(4.5-5]\''

'\'(5-5.5]\''

'\'(5.5-6]\''

'\'(6-6.5]\''

'\'(6.5-inf)\''}

@attribute wage-increase-third-year {'\'(-inf-2.31]\''

'\'(2.31-2.62]\''

'\'(2.62-2.93]\''

'\'(2.93-3.24]\''

'\'(3.24-3.55]\''

'\'(3.55-3.86]\''

'\'(3.86-4.17]\''

'\'(4.17-4.48]\''

'\'(4.48-4.79]\''

'\'(4.79-inf)\''}

@attribute cost-of-living-adjustment {none

tcf

tc}

@attribute working-hours {'\'(-inf-28.3]\''

'\'(28.3-29.6]\''

'\'(29.6-30.9]\''

'\'(30.9-32.2]\''

'\'(32.2-33.5]\''

'\'(33.5-34.8]\''

'\'(34.8-36.1]\''

'\'(36.1-37.4]\''

'\'(37.4-38.7]\''

'\'(38.7-inf)\''}

@attribute pension {none

ret_allw

empl_contr}

@attribute standby-pay {'\'(-inf-3.2]\''

'\'(3.2-4.4]\''

'\'(4.4-5.6]\''

'\'(5.6-6.8]\''

'\'(6.8-8]\''

'\'(8-9.2]\''

'\'(9.2-10.4]\''

'\'(10.4-11.6]\''

'\'(11.6-12.8]\''

'\'(12.8-inf)\''}

@attribute shift-differential {'\'(-inf-2.5]\''

'\'(2.5-5]\''

'\'(5-7.5]\''

'\'(7.5-10]\''

'\'(10-12.5]\''

'\'(12.5-15]\''

'\'(15-17.5]\''

'\'(17.5-20]\''

'\'(20-22.5]\''

'\'(22.5-inf)\''}

@attribute education-allowance {yes

no}

@attribute statutory-holidays {'\'(-inf-9.6]\''

'\'(9.6-10.2]\''

'\'(10.2-10.8]\''

'\'(10.8-11.4]\''

'\'(11.4-12]\''

'\'(12-12.6]\''

'\'(12.6-13.2]\''

'\'(13.2-13.8]\''

'\'(13.8-14.4]\''

'\'(14.4-inf)\''}

@attribute vacation {below_average

average

generous}

@attribute longterm-disability-assistance {yes

no}

@attribute contribution-to-dental-plan {none

half

full}

@attribute bereavement-assistance {yes

no}

@attribute contribution-to-health-plan {none

half

full}

@attribute class {bad

good}

@data

'\'(-inf-1.2]\''

'\'(4.5-5]\''

'\'(38.7-inf)\''

'\'(-inf-2.5]\''

'\'(10.8-11.4]\''

average

yes

good

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(5.5-6]\''

'\'(34.8-36.1]\''

ret_allw

yes

'\'(10.8-11.4]\''

below_average

full

good

'\'(37.4-38.7]\''

empl_contr

'\'(2.5-5]\''

'\'(10.8-11.4]\''

generous

yes

half

yes

half

good

'\'(2.8-inf)\''

'\'(3.5-4]\''

'\'(4.79-inf)\''

yes

good

'\'(2.8-inf)\''

'\'(4-4.5]\''

'\'(4.79-inf)\''

'\'(38.7-inf)\''

'\'(11.4-12]\''

average

half

yes

half

good

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(34.8-36.1]\''

'\'(5-7.5]\''

yes

'\'(11.4-12]\''

average

good

'\'(2.8-inf)\''

'\'(3.5-4]\''

'\'(4.5-5]\''

'\'(4.79-inf)\''

empl_contr

'\'(11.4-12]\''

generous

yes

none

yes

half

good

'\'(2.8-inf)\''

'\'(6.5-inf)\''

'\'(4.5-5]\''

'\'(-inf-2.31]\''

'\'(38.7-inf)\''

'\'(2.5-5]\''

'\'(11.4-12]\''

below_average

good

'\'(1.8-2]\''

'\'(2.5-3]\''

'\'(6.5-inf)\''

'\'(37.4-38.7]\''

'\'(11.6-12.8]\''

'\'(22.5-inf)\''

yes

'\'(10.8-11.4]\''

below_average

yes

half

yes

good

'\'(-inf-1.2]\''

'\'(5.5-6]\''

none

'\'(38.7-inf)\''

empl_contr

'\'(2.5-5]\''

'\'(10.8-11.4]\''

generous

yes

full

good

'\'(2.8-inf)\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4.48-4.79]\''

none

'\'(34.8-36.1]\''

'\'(2.5-5]\''

'\'(12.6-13.2]\''

generous

yes

full

good

'\'(1.8-2]\''

'\'(6-6.5]\''

'\'(37.4-38.7]\''

'\'(2.5-5]\''

'\'(14.4-inf)\''

full

good

'\'(1.8-2]\''

'\'(3-3.5]\''

'\'(3.5-4]\''

none

'\'(38.7-inf)\''

'\'(-inf-2.5]\''

'\'(9.6-10.2]\''

below_average

half

bad

'\'(2.8-inf)\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4.79-inf)\''

tcf

'\'(36.1-37.4]\''

'\'(2.5-5]\''

'\'(12.6-13.2]\''

generous

full

yes

full

good

'\'(-inf-1.2]\''

'\'(2.5-3]\''

none

'\'(34.8-36.1]\''

'\'(7.5-10]\''

'\'(10.8-11.4]\''

generous

good

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(3.5-4]\''

none

'\'(36.1-37.4]\''

empl_contr

'\'(10.8-11.4]\''

average

full

yes

good

'\'(-inf-1.2]\''

'\'(2.5-3]\''

'\'(34.8-36.1]\''

'\'(-inf-2.5]\''

'\'(11.4-12]\''

below_average

good

'\'(-inf-1.2]\''

'\'(-inf-2.5]\''

'\'(38.7-inf)\''

ret_allw

'\'(-inf-3.2]\''

'\'(2.5-5]\''

'\'(-inf-9.6]\''

below_average

yes

half

none

bad

'\'(-inf-1.2]\''

'\'(-inf-2.5]\''

none

'\'(37.4-38.7]\''

none

yes

'\'(10.8-11.4]\''

average

none

bad

'\'(1.8-2]\''

'\'(3.5-4]\''

'\'(4.5-5]\''

tcf

'\'(34.8-36.1]\''

'\'(12.8-inf)\''

'\'(2.5-5]\''

'\'(14.4-inf)\''

generous

good

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(37.4-38.7]\''

'\'(2.5-5]\''

'\'(11.4-12]\''

generous

full

good

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(2.5-3]\''

'\'(38.7-inf)\''

none

'\'(10.8-11.4]\''

below_average

bad

'\'(2.8-inf)\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4.48-4.79]\''

tcf

'\'(-inf-28.3]\''

good

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(3.5-4]\''

'\'(38.7-inf)\''

'\'(2.5-5]\''

'\'(9.6-10.2]\''

generous

half

full

good

'\'(-inf-1.2]\''

'\'(5.5-6]\''

'\'(37.4-38.7]\''

'\'(6.8-8]\''

'\'(2.5-5]\''

'\'(-inf-9.6]\''

generous

good

'\'(2.8-inf)\''

'\'(-inf-2.5]\''

'\'(-inf-2.31]\''

none

'\'(38.7-inf)\''

none

'\'(9.6-10.2]\''

below_average

half

yes

full

bad

'\'(1.8-2]\''

'\'(4-4.5]\''

tcf

yes

'\'(9.6-10.2]\''

below_average

yes

none

half

good

'\'(1.8-2]\''

'\'(2.5-3]\''

none

'\'(32.2-33.5]\''

yes

'\'(11.4-12]\''

generous

yes

full

good

'\'(1.8-2]\''

'\'(4.5-5]\''

'\'(3.5-4]\''

none

'\'(36.1-37.4]\''

'\'(2.5-5]\''

'\'(10.8-11.4]\''

below_average

yes

full

yes

full

good

'\'(2.8-inf)\''

'\'(-inf-2.5]\''

'\'(34.8-36.1]\''

none

'\'(9.6-10.2]\''

average

yes

full

bad

'\'(2.8-inf)\''

'\'(4-4.5]\''

'\'(4.79-inf)\''

none

'\'(38.7-inf)\''

'\'(10.8-11.4]\''

average

half

good

'\'(2.8-inf)\''

'\'(2.5-3]\''

'\'(-inf-2.5]\''

'\'(2.31-2.62]\''

'\'(38.7-inf)\''

none

'\'(2.5-5]\''

'\'(9.6-10.2]\''

below_average

yes

half

yes

full

bad

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(37.4-38.7]\''

empl_contr

'\'(9.6-10.2]\''

average

bad

'\'(1.8-2]\''

'\'(3.5-4]\''

'\'(4.5-5]\''

none

'\'(38.7-inf)\''

none

'\'(2.5-5]\''

'\'(9.6-10.2]\''

below_average

none

bad

'\'(2.8-inf)\''

'\'(-inf-2.5]\''

'\'(-inf-2.31]\''

'\'(38.7-inf)\''

none

'\'(-inf-3.2]\''

'\'(-inf-2.5]\''

'\'(9.6-10.2]\''

below_average

half

yes

full

bad

'\'(1.8-2]\''

'\'(-inf-2.5]\''

none

'\'(38.7-inf)\''

none

'\'(10.8-11.4]\''

average

yes

none

yes

full

bad

'\'(-inf-1.2]\''

'\'(-inf-2.5]\''

'\'(38.7-inf)\''

ret_allw

'\'(3.2-4.4]\''

'\'(-inf-2.5]\''

'\'(10.8-11.4]\''

generous

none

bad

'\'(-inf-1.2]\''

'\'(2.5-3]\''

none

'\'(37.4-38.7]\''

empl_contr

'\'(-inf-3.2]\''

'\'(2.5-5]\''

'\'(-inf-9.6]\''

below_average

yes

half

none

bad

'\'(2.8-inf)\''

'\'(-inf-2.5]\''

'\'(-inf-2.31]\''

'\'(36.1-37.4]\''

empl_contr

'\'(9.6-10.2]\''

average

yes

none

bad

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(3.5-4]\''

none

'\'(38.7-inf)\''

'\'(2.5-5]\''

'\'(11.4-12]\''

average

yes

full

yes

half

good

'\'(-inf-1.2]\''

'\'(3.5-4]\''

none

yes

'\'(10.8-11.4]\''

average

none

bad

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(2.5-3]\''

none

'\'(37.4-38.7]\''

empl_contr

yes

'\'(11.4-12]\''

generous

yes

none

yes

full

bad

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(38.7-inf)\''

empl_contr

'\'(11.4-12]\''

average

yes

bad

'\'(1.8-2]\''

'\'(-inf-2.5]\''

'\'(2.5-3]\''

tcf

'\'(38.7-inf)\''

none

'\'(10.8-11.4]\''

below_average

yes

bad

'\'(1.8-2]\''

'\'(3.5-4]\''

none

'\'(38.7-inf)\''

none

'\'(2.5-5]\''

'\'(9.6-10.2]\''

below_average

none

bad

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(3.5-4]\''

'\'(38.7-inf)\''

'\'(-inf-2.5]\''

'\'(9.6-10.2]\''

below_average

half

bad

'\'(1.8-2]\''

'\'(4-4.5]\''

'\'(3.5-4]\''

none

'\'(38.7-inf)\''

'\'(2.5-5]\''

'\'(10.8-11.4]\''

average

full

yes

full

good

'\'(1.8-2]\''

'\'(4.5-5]\''

tcf

'\'(37.4-38.7]\''

yes

half

good

'\'(1.8-2]\''

'\'(4.5-5]\''

'\'(4-4.5]\''

none

'\'(37.4-38.7]\''

'\'(12.8-inf)\''

'\'(2.5-5]\''

'\'(10.8-11.4]\''

below_average

yes

full

good

'\'(1.8-2]\''

'\'(5.5-6]\''

'\'(4-4.5]\''

none

'\'(38.7-inf)\''

ret_allw

'\'(10.8-11.4]\''

average

yes

full

yes

full

good

'\'(1.8-2]\''

'\'(6.5-inf)\''

'\'(5-5.5]\''

'\'(10.8-11.4]\''

yes

full

good

'\'(2.8-inf)\''

'\'(-inf-2.5]\''

'\'(2.5-3]\''

tcf

empl_contr

yes

half

yes

good

'\'(2.8-inf)\''

'\'(3-3.5]\''

'\'(3.5-4]\''

'\'(4.48-4.79]\''

tcf

'\'(34.8-36.1]\''

'\'(12.6-13.2]\''

generous

yes

full

good

'\'(2.8-inf)\''

'\'(3.5-4]\''

'\'(3-3.5]\''

none

'\'(38.7-inf)\''

empl_contr

'\'(5-7.5]\''

'\'(10.8-11.4]\''

average

yes

full

good

'\'(2.8-inf)\''

'\'(4.5-5]\''

'\'(4-4.5]\''

none

'\'(37.4-38.7]\''

empl_contr

'\'(9.2-10.4]\''

'\'(5-7.5]\''

'\'(10.8-11.4]\''

generous

yes

full

good

'\'(2.8-inf)\''

'\'(4.5-5]\''

'\'(4.79-inf)\''

'\'(38.7-inf)\''

'\'(11.4-12]\''

average

half

yes

half

good

'\'(2.8-inf)\''

'\'(5.5-6]\''

'\'(3.86-4.17]\''

'\'(34.8-36.1]\''

'\'(12.5-15]\''

'\'(-inf-9.6]\''

generous

yes

full

yes

full

good

Data_Files_Arff/monk1.arff

@RELATION monk1
@ATTRIBUTE attribute#1 {1	2	3}
@ATTRIBUTE attribute#2 {1	2	3}
@ATTRIBUTE attribute#3 {1	2}
@ATTRIBUTE attribute#4 {1	2	3}
@ATTRIBUTE attribute#5 {1	2	3	4}
@ATTRIBUTE attribute#6 {1	2}
@ATTRIBUTE class {0	1}
@DATA
1	1	1	1	3	1	1
1	1	1	1	3	2	1
1	1	1	3	2	1	1
1	1	1	3	3	2	1
1	1	2	1	2	1	1
1	1	2	1	2	2	1
1	1	2	2	3	1	1
1	1	2	2	4	1	1
1	1	2	3	1	2	1
1	2	1	1	1	2	1
1	2	1	1	2	1	0
1	2	1	1	3	1	0
1	2	1	1	4	2	0
1	2	1	2	1	1	1
1	2	1	2	3	1	0
1	2	1	2	3	2	0
1	2	1	2	4	2	0
1	2	1	3	2	1	0
1	2	1	3	4	2	0
1	2	2	1	2	2	0
1	2	2	2	3	2	0
1	2	2	2	4	1	0
1	2	2	2	4	2	0
1	2	2	3	2	2	0
1	2	2	3	3	1	0
1	2	2	3	3	2	0
1	3	1	1	2	1	0
1	3	1	1	4	1	0
1	3	1	2	2	1	0
1	3	1	2	4	1	0
1	3	1	3	1	2	1
1	3	1	3	2	2	0
1	3	1	3	3	1	0
1	3	1	3	4	1	0
1	3	1	3	4	2	0
1	3	2	1	2	2	0
1	3	2	2	1	2	1
1	3	2	2	2	2	0
1	3	2	2	3	2	0
1	3	2	2	4	1	0
1	3	2	2	4	2	0
1	3	2	3	1	1	1
1	3	2	3	2	1	0
1	3	2	3	4	1	0
1	3	2	3	4	2	0
2	1	1	1	3	1	0
2	1	1	1	3	2	0
2	1	1	2	1	1	1
2	1	1	2	1	2	1
2	1	1	2	2	2	0
2	1	1	2	3	1	0
2	1	1	2	4	1	0
2	1	1	2	4	2	0
2	1	1	3	4	1	0
2	1	2	1	2	2	0
2	1	2	1	3	1	0
2	1	2	1	4	2	0
2	1	2	2	3	1	0
2	1	2	2	4	2	0
2	1	2	3	2	2	0
2	1	2	3	4	1	0
2	2	1	1	2	1	1
2	2	1	1	2	2	1
2	2	1	1	3	1	1
2	2	1	2	3	2	1
2	2	1	3	1	1	1
2	2	1	3	1	2	1
2	2	1	3	2	2	1
2	2	1	3	3	2	1
2	2	1	3	4	2	1
2	2	2	1	1	1	1
2	2	2	1	3	2	1
2	2	2	1	4	1	1
2	2	2	1	4	2	1
2	2	2	2	2	1	1
2	2	2	3	4	1	1
2	3	1	1	1	1	1
2	3	1	2	1	1	1
2	3	1	2	3	1	0
2	3	1	3	1	2	1
2	3	1	3	3	1	0
2	3	1	3	4	2	0
2	3	2	1	3	2	0
2	3	2	2	1	1	1
2	3	2	2	1	2	1
2	3	2	2	2	1	0
2	3	2	3	3	2	0
3	1	1	1	1	1	1
3	1	1	1	1	2	1
3	1	1	2	1	1	1
3	1	1	2	2	2	0
3	1	1	3	2	2	0
3	1	2	1	1	1	1
3	1	2	1	2	2	0
3	1	2	2	2	2	0
3	1	2	2	3	2	0
3	1	2	3	2	2	0
3	2	1	1	1	1	1
3	2	1	1	4	2	0
3	2	1	2	1	2	1
3	2	1	2	4	2	0
3	2	2	1	1	1	1
3	2	2	1	1	2	1
3	2	2	1	3	2	0
3	2	2	3	1	1	1
3	2	2	3	2	1	0
3	2	2	3	4	1	0
3	3	1	1	1	1	1
3	3	1	1	2	1	1
3	3	1	1	4	2	1
3	3	1	2	3	2	1
3	3	1	2	4	2	1
3	3	1	3	1	2	1
3	3	1	3	2	1	1
3	3	1	3	2	2	1
3	3	1	3	4	2	1
3	3	2	1	1	1	1
3	3	2	1	3	2	1
3	3	2	1	4	1	1
3	3	2	1	4	2	1
3	3	2	3	1	2	1
3	3	2	3	2	2	1
3	3	2	3	3	2	1
3	3	2	3	4	2	1

Data_Files_Arff/monk2.arff

@RELATION monk2
@ATTRIBUTE attribute#1 {1	2	3}
@ATTRIBUTE attribute#2 {1	2	3}
@ATTRIBUTE attribute#3 {1	2}
@ATTRIBUTE attribute#4 {1	2	3}
@ATTRIBUTE attribute#5 {1	2	3	4}
@ATTRIBUTE attribute#6 {1	2}
@ATTRIBUTE class {0	1}
@DATA
1	1	1	1	2	2	0
1	1	1	1	4	1	0
1	1	1	2	1	1	0
1	1	1	2	1	2	0
1	1	1	2	2	1	0
1	1	1	2	3	1	0
1	1	1	2	4	1	0
1	1	1	3	2	1	0
1	1	1	3	4	1	0
1	1	2	1	1	1	0
1	1	2	1	1	2	0
1	1	2	2	3	1	0
1	1	2	2	4	1	0
1	1	2	2	4	2	1
1	1	2	3	1	2	0
1	1	2	3	2	2	1
1	2	1	1	1	2	0
1	2	1	2	1	2	0
1	2	1	2	2	2	1
1	2	1	2	3	1	0
1	2	1	2	3	2	1
1	2	1	2	4	1	0
1	2	1	3	1	1	0
1	2	1	3	1	2	0
1	2	1	3	2	2	1
1	2	1	3	3	1	0
1	2	1	3	3	2	1
1	2	1	3	4	1	0
1	2	1	3	4	2	1
1	2	2	1	2	1	0
1	2	2	1	4	1	0
1	2	2	2	3	1	1
1	2	2	2	4	1	1
1	2	2	3	1	1	0
1	2	2	3	1	2	1
1	2	2	3	3	1	1
1	2	2	3	3	2	0
1	2	2	3	4	1	1
1	2	2	3	4	2	0
1	3	1	1	1	2	0
1	3	1	1	2	2	0
1	3	1	1	3	1	0
1	3	1	1	3	2	0
1	3	1	2	2	1	0
1	3	1	2	2	2	1
1	3	1	2	3	2	1
1	3	1	2	4	1	0
1	3	1	3	2	2	1
1	3	1	3	3	1	0
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1	3	2	1	3	1	0
1	3	2	1	3	2	1
1	3	2	1	4	1	0
1	3	2	2	1	2	1
1	3	2	2	3	2	0
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1	3	2	3	2	1	1
2	1	1	1	1	1	0
2	1	1	1	2	2	0
2	1	1	1	3	1	0
2	1	1	2	2	2	1
2	1	1	3	1	2	0
2	1	1	3	2	2	1
2	1	1	3	3	2	1
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2	1	2	1	1	1	0
2	1	2	1	2	2	1
2	1	2	1	4	1	0
2	1	2	2	2	1	1
2	1	2	2	4	2	0
2	1	2	3	1	1	0
2	1	2	3	1	2	1
2	1	2	3	2	2	0
2	1	2	3	3	2	0
2	1	2	3	4	2	0
2	2	1	1	3	1	0
2	2	1	1	4	2	1
2	2	1	2	1	1	0
2	2	1	2	3	1	1
2	2	1	3	3	1	1
2	2	1	3	3	2	0
2	2	1	3	4	1	1
2	2	2	1	1	1	0
2	2	2	1	2	2	0
2	2	2	1	3	2	0
2	2	2	1	4	1	1
2	2	2	1	4	2	0
2	2	2	2	1	1	1
2	2	2	2	2	2	0
2	2	2	2	3	1	0
2	2	2	3	1	1	1
2	2	2	3	2	1	0
2	2	2	3	2	2	0
2	2	2	3	4	2	0
2	3	1	1	1	1	0
2	3	1	1	1	2	0
2	3	1	1	3	2	1
2	3	1	2	1	1	0
2	3	1	2	3	1	1
2	3	1	2	3	2	0
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2	3	1	3	1	2	1
2	3	1	3	2	1	1
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2	3	2	2	1	1	1
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2	3	2	2	3	2	0
2	3	2	3	3	1	0
2	3	2	3	3	2	0
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3	1	1	1	4	1	0
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3	1	1	2	2	2	1
3	1	1	2	3	2	1
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3	1	1	2	4	2	1
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3	1	1	3	1	2	0
3	1	1	3	2	2	1
3	1	1	3	3	2	1
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3	1	2	1	2	2	1
3	1	2	1	3	1	0
3	1	2	1	3	2	1
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3	1	2	1	4	2	1
3	1	2	2	2	1	1
3	1	2	3	1	2	1
3	1	2	3	2	1	1
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3	1	2	3	4	2	0
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3	2	1	1	2	2	1
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3	2	1	1	3	2	1
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3	2	1	3	2	1	1
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3	2	2	1	2	2	0
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3	2	2	2	1	1	1
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3	2	2	2	2	2	0
3	2	2	2	3	2	0
3	2	2	3	1	1	1
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3	2	2	3	4	2	0
3	3	1	1	1	1	0
3	3	1	1	2	1	0
3	3	1	1	3	1	0
3	3	1	1	3	2	1
3	3	1	2	3	2	0
3	3	2	1	1	1	0
3	3	2	2	1	1	1
3	3	2	2	2	1	0
3	3	2	2	3	1	0
3	3	2	2	3	2	0
3	3	2	3	1	1	1
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3	3	2	3	4	2	0

Data_Files_Arff/prnn_virus3.arff

% Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley % ===================================================================== % % Cambridge University Press (1996) ISBN 0-521-46086-7 % % The background to the datasets is described in section 1.4; this file % relates the computer-readable files to that description. % % % % Cushing's syndrome % ------------------ % % Data from Aitchison & Dunsmore (1975, Tables 11.1-3). % % Data file Cushings.dat has four columns, % % Label of the patient % Tetrhydrocortisone (mg/24hr) % Pregnanetriol (mg/24hr) % Type % % The type of the last six patients (u1 to u6) should be % regarded as unknown. (The code `o' indicates `other'). % % % % synthetic two-class problem % --------------------------- % % Data from Ripley (1994a). % % This has two real-valued co-ordinates (xs and ys) and a class (xc) % which is 0 or 1. % % Data file synth.tr has 250 rows of the training set % synth.te has 1000 rows of the test set (not used here) % % % % viruses % ------- % % This is a dataset on 61 viruses with rod-shaped particles affecting % various crops (tobacco, tomato, cucumber and others) described by % {Fauquet et al. (1988) and analysed by Eslava-G\'omez (1989). There % are 18 measurements on each virus, the number of amino acid residues % per molecule of coat protein. % % Data file viruses.dat has 61 rows of 18 counts % virus3.dat has 38 rows corresponding to the distinct % Tobamoviruses. % % The whole dataset is in order Hordeviruses (3), Tobraviruses (6), % Tobamoviruses (39) and `furoviruses' (13). % % % % Leptograpsus crabs % ------------------ % % Data from Campbell & Mahon (1974) on the morphology of rock crabs of % genus Leptograpsus. % % There are 50 specimens of each sex of each of two colour forms. % % Data file crabs.dat has rows % % sp `species', coded B (blue form) or O (orange form) % sex coded M or F % index within each group of 50 % FL frontal lip of carapace (mm) % RW rear width of carapace (mm) % CL length along the midline of carapace (mm) % CW maximum width of carapace (mm) % BD body depth (mm) % % % % Forensic glass % -------------- % % This example comes from forensic testing of glass collected by % B. German on 214 fragments of glass. It is also contained in the % UCI machine-learning database collection (Murphy & Aha, 1995). % % Data file fglass.dat has 214 rows with data for a single glass % fragment. % % RI refractive index % Na % weight of sodium oxide(s) % Mg % weight of magnesium oxide(s) % Al % weight of aluminium oxide(s) % Si % weight of silicon oxide(s) % K % weight of potassium oxide(s) % Ca % weight of calcium oxide(s) % Ba % weight of barium oxide(s) % Fe % weight of iron oxide(s) % type coded 1 to 7 % % The type codes are: % % 1 (WinF) window float glass % 2 (WinNF) window non-float glass % 3 (Veh) vehicle glass % 5 (Con) containers % 6 (Tabl) tableware % 7 (Head) vehicle headlamp glass % % The ten groups used for the cross-validation experiments (I believe) % are listed as row numbers in the file fglass.grp, % % % % Diabetes in Pima Indians % ------------------------ % % A population of women who were at least 21 years old, of Pima Indian heritage % and living near Phoenix, Arizona, was tested for diabetes % according to World Health Organization criteria. The data % were collected by the US National Institute of Diabetes and Digestive and % Kidney Diseases (Smith et al, 1988). This example is also contained in the % UCI machine-learning database collection (Murphy & Aha, 1995). % % The data files have rows containing % % npreg number of pregnancies % glu plasma glucose concentration in an oral glucose tolerance test % bp diastolic blood pressure (mm Hg) % skin triceps skin fold thickness (mm) % ins serum insulin (micro U/ml) % bmi body mass index (weight in kg/(height in m)^2) % ped diabetes pedigree function % age in years % type No / Yes % % Data file pima.tr has 200 rows of complete training data. % pima.te has 332 rows of complete test data. % pima.tr2 has the 200 rows of pima.tr plus 100 incomplete rows. % % % Note: there were no column information in the data, hence the names % were generated automatically % % % Information about the dataset % CLASSTYPE: nominal % CLASSINDEX: none specific % @relation prnn-virus3 @attribute col_1 INTEGER @attribute col_2 INTEGER @attribute col_3 INTEGER @attribute col_4 INTEGER @attribute col_5 {4,6,7,8,9,10,11} @attribute col_6 {3,4,5,6,7,8,9} @attribute col_7 {11,12,13,14,15,17,18,19,20,21} @attribute col_8 {0,1,2} @attribute col_9 {7,8,9,10,11,12,13,14,15} @attribute col_10 {0,1,2,3,4} @attribute col_11 {4,5,6,7,8,9,10,11} @attribute col_12 {11,12,13,14,15,16,18} @attribute col_13 {4,5,6,7,8} @attribute col_14 {5,6,7,8,9,10,11,12} @attribute col_15 {0,1,2} @attribute col_16 {1,2,3,4} @attribute col_17 {8,9,10,11,12} @attribute col_18 {1,2,3,5} @data 17,13,14,16,4,9,14,1,13,0,11,13,5,7,1,4,11,5 12,11,9,12,6,5,12,1,9,1,7,12,5,6,0,4,8,2 18,16,16,16,8,6,14,1,14,0,9,12,4,8,0,2,11,3 18,16,15,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 17,13,13,22,8,4,18,1,10,3,8,11,7,6,1,2,10,2 16,13,16,21,9,3,17,1,10,4,7,12,7,5,1,2,11,3 22,19,10,16,10,4,18,1,12,2,8,11,6,8,0,1,8,2 20,10,24,10,6,9,21,0,7,0,7,18,4,9,1,4,8,2 20,21,12,15,9,7,11,1,9,3,8,14,6,7,0,1,10,3 20,21,12,15,9,7,11,1,9,3,9,14,5,7,0,1,10,3 18,11,24,10,9,6,19,0,12,0,7,14,4,11,0,4,9,1 20,12,23,10,8,5,20,0,13,0,6,13,4,11,0,4,10,1 18,19,18,16,8,4,12,0,12,0,10,15,8,6,1,1,12,1 17,16,17,15,8,6,14,1,14,0,9,12,4,8,0,3,11,3 19,17,14,16,8,6,14,1,14,0,8,12,4,8,0,2,12,3 19,17,15,16,8,5,14,1,14,0,8,12,4,8,0,2,12,3 19,15,16,16,8,6,14,1,15,0,8,12,4,8,0,2,12,3 17,17,16,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 18,17,15,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 22,19,10,16,10,5,17,1,12,2,8,11,6,8,0,1,8,2 18,20,10,18,6,8,17,1,14,1,5,16,4,7,0,2,9,2 18,16,16,15,8,6,13,1,14,1,8,12,4,8,1,2,12,3 20,21,12,15,9,7,11,1,10,3,8,14,7,7,0,1,9,3 20,21,12,15,9,7,11,1,10,3,9,14,5,7,0,1,10,3 18,12,23,10,9,5,20,0,14,0,7,12,4,11,0,4,10,1 18,12,21,10,10,5,18,0,13,0,8,12,4,12,0,4,10,1 17,12,22,10,8,5,18,0,14,0,5,13,4,10,0,3,9,1 17,16,16,16,8,6,15,1,14,0,9,12,4,8,0,2,11,3 19,17,15,17,7,6,15,1,14,0,8,12,4,8,0,2,10,3 18,16,16,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 18,17,15,17,8,6,15,1,14,0,8,12,4,8,0,3,9,3 15,12,14,23,8,3,17,1,9,4,7,15,6,6,1,2,11,2 13,11,14,22,7,3,17,1,10,4,8,13,6,6,1,3,11,2 16,11,15,23,10,4,18,1,10,3,7,12,6,5,1,2,9,3 14,11,14,25,11,3,19,2,10,2,7,12,6,5,1,2,9,3 11,11,15,24,10,5,18,1,11,1,7,14,5,7,2,3,11,2 15,9,12,21,8,4,21,1,10,3,7,15,7,6,1,3,10,3 15,11,15,22,7,3,19,1,8,3,4,14,6,5,1,2,10,2

Data_Files_Arff/prnn_viruses_Pattern-Recognition.arff

% Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley % ===================================================================== % % Cambridge University Press (1996) ISBN 0-521-46086-7 % % The background to the datasets is described in section 1.4; this file % relates the computer-readable files to that description. % % % % Cushing's syndrome % ------------------ % % Data from Aitchison & Dunsmore (1975, Tables 11.1-3). % % Data file Cushings.dat has four columns, % % Label of the patient % Tetrhydrocortisone (mg/24hr) % Pregnanetriol (mg/24hr) % Type % % The type of the last six patients (u1 to u6) should be % regarded as unknown. (The code `o' indicates `other'). % % % % synthetic two-class problem % --------------------------- % % Data from Ripley (1994a). % % This has two real-valued co-ordinates (xs and ys) and a class (xc) % which is 0 or 1. % % Data file synth.tr has 250 rows of the training set % synth.te has 1000 rows of the test set (not used here) % % % % viruses % ------- % % This is a dataset on 61 viruses with rod-shaped particles affecting % various crops (tobacco, tomato, cucumber and others) described by % {Fauquet et al. (1988) and analysed by Eslava-G\'omez (1989). There % are 18 measurements on each virus, the number of amino acid residues % per molecule of coat protein. % % Data file viruses.dat has 61 rows of 18 counts % virus3.dat has 38 rows corresponding to the distinct % Tobamoviruses. % % The whole dataset is in order Hordeviruses (3), Tobraviruses (6), % Tobamoviruses (39) and `furoviruses' (13). % % % % Leptograpsus crabs % ------------------ % % Data from Campbell & Mahon (1974) on the morphology of rock crabs of % genus Leptograpsus. % % There are 50 specimens of each sex of each of two colour forms. % % Data file crabs.dat has rows % % sp `species', coded B (blue form) or O (orange form) % sex coded M or F % index within each group of 50 % FL frontal lip of carapace (mm) % RW rear width of carapace (mm) % CL length along the midline of carapace (mm) % CW maximum width of carapace (mm) % BD body depth (mm) % % % % Forensic glass % -------------- % % This example comes from forensic testing of glass collected by % B. German on 214 fragments of glass. It is also contained in the % UCI machine-learning database collection (Murphy & Aha, 1995). % % Data file fglass.dat has 214 rows with data for a single glass % fragment. % % RI refractive index % Na % weight of sodium oxide(s) % Mg % weight of magnesium oxide(s) % Al % weight of aluminium oxide(s) % Si % weight of silicon oxide(s) % K % weight of potassium oxide(s) % Ca % weight of calcium oxide(s) % Ba % weight of barium oxide(s) % Fe % weight of iron oxide(s) % type coded 1 to 7 % % The type codes are: % % 1 (WinF) window float glass % 2 (WinNF) window non-float glass % 3 (Veh) vehicle glass % 5 (Con) containers % 6 (Tabl) tableware % 7 (Head) vehicle headlamp glass % % The ten groups used for the cross-validation experiments (I believe) % are listed as row numbers in the file fglass.grp, % % % % Diabetes in Pima Indians % ------------------------ % % A population of women who were at least 21 years old, of Pima Indian heritage % and living near Phoenix, Arizona, was tested for diabetes % according to World Health Organization criteria. The data % were collected by the US National Institute of Diabetes and Digestive and % Kidney Diseases (Smith et al, 1988). This example is also contained in the % UCI machine-learning database collection (Murphy & Aha, 1995). % % The data files have rows containing % % npreg number of pregnancies % glu plasma glucose concentration in an oral glucose tolerance test % bp diastolic blood pressure (mm Hg) % skin triceps skin fold thickness (mm) % ins serum insulin (micro U/ml) % bmi body mass index (weight in kg/(height in m)^2) % ped diabetes pedigree function % age in years % type No / Yes % % Data file pima.tr has 200 rows of complete training data. % pima.te has 332 rows of complete test data. % pima.tr2 has the 200 rows of pima.tr plus 100 incomplete rows. % % % Note: there were no column information in the data, hence the names % were generated automatically % % % Information about the dataset % CLASSTYPE: nominal % CLASSINDEX: none specific % @relation prnn-viruses @attribute col_1 INTEGER @attribute col_2 INTEGER @attribute col_3 INTEGER @attribute col_4 INTEGER @attribute col_5 INTEGER @attribute col_6 INTEGER @attribute col_7 INTEGER @attribute col_8 {0,1,2,3} @attribute col_9 INTEGER @attribute col_10 {0,1,2,3,4,7} @attribute col_11 {3,4,5,6,7,8,9,10,11} @attribute col_12 {11,12,13,14,15,16,17,18,19,21} @attribute col_13 {4,5,6,7,8,9,12,15} @attribute col_14 {4,5,6,7,8,9,10,11,12,14} @attribute col_15 {0,1,2,3,4,5,6} @attribute col_16 INTEGER @attribute col_17 INTEGER @attribute col_18 {0,1,2,3,4,5} @data 25,9,9,19,12,8,20,0,10,0,6,21,8,7,4,7,17,5 26,9,9,20,13,8,20,0,10,0,6,21,8,7,4,7,17,5 25,9,9,22,10,10,23,0,13,0,6,19,5,6,4,8,16,5 15,10,21,13,18,12,22,1,9,2,4,11,5,10,1,14,8,2 17,11,22,15,14,10,23,1,11,2,4,11,5,9,1,13,9,1 22,17,17,16,10,15,13,1,7,2,3,14,9,9,2,12,6,2 21,18,18,15,11,15,16,1,7,2,3,14,6,8,2,12,7,2 20,9,16,15,16,6,19,1,7,3,4,14,4,11,1,16,11,3 22,10,17,18,13,6,21,1,8,3,4,13,4,11,1,15,10,3 17,13,14,16,4,9,14,1,13,0,11,13,5,7,1,4,11,5 12,11,9,12,6,5,12,1,9,1,7,12,5,6,0,4,8,2 18,16,16,16,8,6,14,1,14,0,9,12,4,8,0,2,11,3 18,16,15,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 17,13,13,22,8,4,18,1,10,3,8,11,7,6,1,2,10,2 16,13,16,21,9,3,17,1,10,4,7,12,7,5,1,2,11,3 22,19,10,16,10,4,18,1,12,2,8,11,6,8,0,1,8,2 20,10,24,10,6,9,21,0,7,0,7,18,4,9,1,4,8,2 20,21,12,15,9,7,11,1,9,3,8,14,6,7,0,1,10,3 20,21,12,15,9,7,11,1,9,3,9,14,5,7,0,1,10,3 18,11,24,10,9,6,19,0,12,0,7,14,4,11,0,4,9,1 20,12,23,10,8,5,20,0,13,0,6,13,4,11,0,4,10,1 18,19,18,16,8,4,12,0,12,0,10,15,8,6,1,1,12,1 17,16,17,15,8,6,14,1,14,0,9,12,4,8,0,3,11,3 19,17,14,16,8,6,14,1,14,0,8,12,4,8,0,2,12,3 19,17,15,16,8,5,14,1,14,0,8,12,4,8,0,2,12,3 19,15,16,16,8,6,14,1,15,0,8,12,4,8,0,2,12,3 17,17,16,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 18,17,15,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 22,19,10,16,10,4,18,1,12,2,8,11,6,8,0,1,8,2 22,19,10,16,10,5,17,1,12,2,8,11,6,8,0,1,8,2 18,20,10,18,6,8,17,1,14,1,5,16,4,7,0,2,9,2 18,16,16,15,8,6,13,1,14,1,8,12,4,8,1,2,12,3 20,21,12,15,9,7,11,1,10,3,8,14,7,7,0,1,9,3 20,21,12,15,9,7,11,1,10,3,9,14,5,7,0,1,10,3 18,12,23,10,9,5,20,0,14,0,7,12,4,11,0,4,10,1 18,12,21,10,10,5,18,0,13,0,8,12,4,12,0,4,10,1 17,12,22,10,8,5,18,0,14,0,5,13,4,10,0,3,9,1 17,16,16,16,8,6,15,1,14,0,9,12,4,8,0,2,11,3 19,17,15,17,7,6,15,1,14,0,8,12,4,8,0,2,10,3 18,16,16,19,8,6,11,1,15,1,7,13,5,8,0,2,9,3 18,17,15,17,8,6,15,1,14,0,8,12,4,8,0,3,9,3 15,12,14,23,8,3,17,1,9,4,7,15,6,6,1,2,11,2 13,11,14,22,7,3,17,1,10,4,8,13,6,6,1,3,11,2 16,11,15,23,10,4,18,1,10,3,7,12,6,5,1,2,9,3 14,11,14,25,11,3,19,2,10,2,7,12,6,5,1,2,9,3 11,11,15,24,10,5,18,1,11,1,7,14,5,7,2,3,11,2 15,9,12,21,8,4,21,1,10,3,7,15,7,6,1,3,10,3 15,11,15,22,7,3,19,1,8,3,4,14,6,5,1,2,10,2 27,8,13,25,12,26,21,1,20,0,11,18,5,7,5,7,19,3 27,8,13,25,13,27,21,1,19,0,11,18,6,8,5,7,19,3 27,7,12,25,12,26,21,1,20,0,11,17,6,8,5,7,19,3 26,8,13,25,13,26,21,1,19,0,11,18,6,8,5,8,19,3 28,6,13,24,12,30,22,1,18,0,11,18,6,8,4,7,19,3 27,8,14,25,13,26,21,1,18,0,11,18,6,8,5,7,19,3 24,15,18,14,10,14,19,1,14,7,5,19,4,6,2,12,10,4 25,14,15,15,9,12,14,0,8,3,6,12,4,14,1,10,8,0 29,11,12,23,9,15,23,0,16,1,10,13,5,8,3,6,23,5 28,15,22,21,7,32,21,1,13,2,8,16,12,5,5,8,9,1 29,14,22,20,9,20,20,2,15,2,9,16,7,6,6,10,13,2 29,16,18,18,8,32,22,1,14,2,9,18,15,4,4,8,9,1 31,14,21,20,9,21,20,3,15,2,8,17,6,7,6,10,13,1

Data_Files_Arff/shape.arff

% % This is from ML_partIII.pdf (in ~/weka/book/**...) % @relation shape @attribute width real @attribute height real @attribute sides real @attribute class {standing, lying} @data 2,4,4,standing 3,6,4,standing 4,3,4,lying 7,8,3,standing 7,6,3,lying 2,9,4,standing 9,1,4,lying 10,2,3,lying

Data_Files_Arff/ticdata_categ.arff

@relation tic % allting här är kategorier för att kunna köra Apriori etc % hakank: jag låter nu alla attribut (utom de som uttryckligen är givna) % vara i intervallet 0..10 (för enkelhets skull) % see L0 @attribute MOSTYPE_Customer_Subtype {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41} @attribute MAANTHUI_Number_of_houses {0,1,2,3,4,5,6,7,8,9,10,11,12} % see L1 @attribute MGEMOMV_Avg_size_household {0,1,2,3,4,5,6} @attribute MGEMLEEF_Avg_age {1,2,3,4,5,6} % see L2 @attribute MOSHOOFD_Customer_main_type {0,1,2,3,4,5,6,7,8,9,10,11,12} % see L3 @attribute MGODRK_Roman_catholic {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MGODPR_Protestant {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MGODOV_Other_religion {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MGODGE_No_religion {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MRELGE_Married {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MRELSA_Living_together {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MRELOV_Other_relation {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MFALLEEN_Singles {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MFGEKIND_Household_without_children {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MFWEKIND_Household_with_children {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MOPLHOOG_High_level_education {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MOPLMIDD_Medium_level_education {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MOPLLAAG_Lower_level_education {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERHOOG_High_status {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERZELF_Entrepreneur {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERBOER_Farmer {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERMIDD_Middle_management {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERARBG_Skilled_labourers {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MBERARBO_Unskilled_labourers {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MSKA_Social_class_A {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MSKB1_Social_class_B1 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MSKB2_Social_class_B2 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MSKC_Social_class_C {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MSKD_Social_class_D {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MHHUUR_Rented_house {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MHKOOP_Home_owners {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MAUT1_1_car {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MAUT2_2_cars {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MAUT0_No_car {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MZFONDS_National_Health_Service {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MZPART_Private_health_insurance {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINKM30_Income_lesss_30_000 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINK3045_Income_30_45_000 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINK4575_Income_45_75_000 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINK7512_Income_75_122_000 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINK123M_Income_gt_123.000 {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MINKGEM_Average_income {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute MKOOPKLA_Purchasing_power_class {0,1,2,3,4,5,6,7,8,9,10,11,12} % see L4 @attribute PWAPART_Contribution_private_third_party_insurance {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PWABEDR_Contribution_third_party_insurance_firms {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PWALAND_Contribution_third_party_insurane_agriculture {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PPERSAUT_Contribution_car_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PBESAUT_Contribution_delivery_van_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PMOTSCO_Contribution_motorcycle/scooter_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PVRAAUT_Contribution_lorry_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PAANHANG_Contribution_trailer_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PTRACTOR_Contribution_tractor_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PWERKT_Contribution_agricultural_machines_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PBROM_Contribution_moped_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PLEVEN_Contribution_life_insurances {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PPERSONG_Contribution_private_accident_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PGEZONG_Contribution_family_accidents_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PWAOREG_Contribution_disability_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PBRAND_Contribution_fire_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PZEILPL_Contribution_surfboard_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PPLEZIER_Contribution_boat_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PFIETS_Contribution_bicycle_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PINBOED_Contribution_property_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute PBYSTAND_Contribution_social_security_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AWAPART_Number_of_private_third_party_insurance {0,1,2,3,4,5,6,7,8,10,11,12} @attribute AWABEDR_Number_of_third_party_insurance_firms {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AWALAND_Number_of_third_party_insurane_agriculture {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute APERSAUT_Number_of_car_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ABESAUT_Number_of_delivery_van_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AMOTSCO_Number_of_motorcycle/scooter_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AVRAAUT_Number_of_lorry_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AAANHANG_Number_of_trailer_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ATRACTOR_Number_of_tractor_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AWERKT_Number_of_agricultural_machines_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ABROM_Number_of_moped_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ALEVEN_Number_of_life_insurances {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute APERSONG_Number_of_private_accident_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AGEZONG_Number_of_family_accidents_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AWAOREG_Number_of_disability_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ABRAND_Number_of_fire_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AZEILPL_Number_of_surfboard_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute APLEZIER_Number_of_boat_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AFIETS_Number_of_bicycle_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute AINBOED_Number_of_property_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute ABYSTAND_Number_of_social_security_insurance_policies {0,1,2,3,4,5,6,7,8,9,10,11,12} @attribute CARAVAN_Number_of_mobile_home_policies {0,1} @data 33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 37,1,2,2,8,1,4,1,4,6,2,2,0,4,5,0,5,4,0,0,0,5,0,4,0,2,3,5,0,2,7,7,1,2,6,3,2,0,5,2,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 9,1,3,3,3,2,3,2,4,5,2,2,2,3,4,3,4,2,4,0,0,3,1,2,3,2,1,4,0,5,4,9,0,0,7,2,1,5,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 40,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 23,1,2,1,5,0,5,0,5,0,6,3,3,5,2,0,5,4,2,0,0,4,2,2,2,2,2,4,2,9,0,5,3,3,9,0,5,2,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 39,2,3,2,9,2,2,0,5,7,2,0,0,3,6,0,4,5,0,0,0,4,1,5,0,1,4,5,0,6,3,8,0,1,9,0,4,3,3,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 33,1,2,4,8,0,1,3,6,6,0,3,3,3,3,0,1,8,1,1,0,1,8,1,1,1,0,8,1,9,0,5,2,3,7,2,7,2,1,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 11,2,3,3,3,3,5,0,2,7,0,2,2,2,6,0,4,5,2,0,0,3,3,3,1,2,1,4,2,0,9,6,1,2,6,3,2,3,3,1,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 10,1,4,3,3,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 9,1,3,3,3,1,3,2,4,7,1,2,2,3,5,1,7,1,4,0,0,5,1,1,2,3,4,1,0,6,3,7,1,2,4,5,3,4,3,1,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 41,1,3,3,10,0,5,0,4,7,1,1,1,4,5,2,4,4,3,0,1,2,2,2,4,2,1,4,0,5,4,7,2,0,8,1,3,2,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 23,1,1,2,5,0,6,1,2,1,2,6,5,3,1,2,6,2,1,0,0,4,3,2,1,3,2,4,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 38,1,2,3,9,0,6,0,3,7,0,2,0,6,3,2,6,2,2,0,0,4,0,4,2,2,4,2,0,6,3,7,2,0,4,5,0,1,6,2,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 22,2,3,3,5,0,5,0,4,7,0,2,0,2,7,2,1,7,0,2,0,1,1,5,2,0,0,7,0,4,5,6,1,2,7,2,0,6,3,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 13,1,4,2,3,2,4,0,3,7,0,2,1,3,6,5,4,1,6,0,0,3,0,1,5,2,1,2,0,4,5,7,1,2,5,4,2,2,6,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 13,1,3,2,3,1,7,0,2,7,0,2,1,3,6,3,5,1,6,0,0,2,0,1,6,1,3,1,0,4,5,7,2,1,5,4,1,1,8,0,0,5,6,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 13,2,4,3,3,0,4,2,4,8,1,1,1,3,6,1,7,2,4,0,0,3,3,0,1,3,3,3,0,1,8,8,0,1,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 37,1,3,3,8,0,5,0,4,7,2,0,0,3,6,3,5,2,1,0,0,5,2,2,1,3,3,2,1,8,1,9,0,0,5,4,2,3,3,2,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 31,1,4,2,7,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,4,0,5,0,9,0,3,2,4,7,2,9,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 24,2,2,2,5,1,3,2,4,2,4,3,3,3,3,2,5,3,2,0,0,5,3,0,2,1,4,4,0,5,4,7,0,2,9,0,5,3,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 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3,1,2,3,1,0,6,0,3,6,0,3,0,7,2,0,7,2,0,0,0,5,2,3,0,2,3,5,0,0,9,5,2,2,5,4,0,4,4,2,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 36,1,1,2,8,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,4,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0 35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0 33,1,3,4,8,0,6,0,3,5,1,4,3,3,4,0,1,8,1,0,0,2,3,5,1,1,1,4,4,7,2,4,0,5,8,1,5,3,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1 34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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Data_Files_Arff/tic-tac-toe.arff

@relation 'tic-tac-toe'
%@attribute top-left-square {x	o	b}
%@attribute top-middle-square {x	o	b}
%@attribute top-right-square {x	o	b}
%@attribute middle-left-square {x	o	b}
%@attribute middle-middle-square {x	o	b}
%@attribute middle-right-square {x	o	b}
%@attribute bottom-left-square {x	o	b}
%@attribute bottom-middle-square {x	o	b}
%@attribute bottom-right-square {x	o	b}
%@attribute Class {positive	negative}
@attribute '1' {x	o	b}
@attribute '2' {x	o	b}
@attribute '3' {x	o	b}
@attribute '4' {x	o	b}
@attribute '5' {x	o	b}
@attribute '6' {x	o	b}
@attribute '7' {x	o	b}
@attribute '8' {x	o	b}
@attribute '9' {x	o	b}
@attribute class {positive	negative}
@data
x	x	x	x	o	o	x	o	o	positive
x	x	x	x	o	o	o	x	o	positive
x	x	x	x	o	o	o	o	x	positive
x	x	x	x	o	o	o	b	b	positive
x	x	x	x	o	o	b	o	b	positive
x	x	x	x	o	o	b	b	o	positive
x	x	x	x	o	b	o	o	b	positive
x	x	x	x	o	b	o	b	o	positive
x	x	x	x	o	b	b	o	o	positive
x	x	x	x	b	o	o	o	b	positive
x	x	x	x	b	o	o	b	o	positive
x	x	x	x	b	o	b	o	o	positive
x	x	x	o	x	o	x	o	o	positive
x	x	x	o	x	o	o	x	o	positive
x	x	x	o	x	o	o	o	x	positive
x	x	x	o	x	o	o	b	b	positive
x	x	x	o	x	o	b	o	b	positive
x	x	x	o	x	o	b	b	o	positive
x	x	x	o	x	b	o	o	b	positive
x	x	x	o	x	b	o	b	o	positive
x	x	x	o	x	b	b	o	o	positive
x	x	x	o	o	x	x	o	o	positive
x	x	x	o	o	x	o	x	o	positive
x	x	x	o	o	x	o	o	x	positive
x	x	x	o	o	x	o	b	b	positive
x	x	x	o	o	x	b	o	b	positive
x	x	x	o	o	x	b	b	o	positive
x	x	x	o	o	b	x	o	b	positive
x	x	x	o	o	b	x	b	o	positive
x	x	x	o	o	b	o	x	b	positive
x	x	x	o	o	b	o	b	x	positive
x	x	x	o	o	b	b	x	o	positive
x	x	x	o	o	b	b	o	x	positive
x	x	x	o	o	b	b	b	b	positive
x	x	x	o	b	x	o	o	b	positive
x	x	x	o	b	x	o	b	o	positive
x	x	x	o	b	x	b	o	o	positive
x	x	x	o	b	o	x	o	b	positive
x	x	x	o	b	o	x	b	o	positive
x	x	x	o	b	o	o	x	b	positive
x	x	x	o	b	o	o	b	x	positive
x	x	x	o	b	o	b	x	o	positive
x	x	x	o	b	o	b	o	x	positive
x	x	x	o	b	o	b	b	b	positive
x	x	x	o	b	b	x	o	o	positive
x	x	x	o	b	b	o	x	o	positive
x	x	x	o	b	b	o	o	x	positive
x	x	x	o	b	b	o	b	b	positive
x	x	x	o	b	b	b	o	b	positive
x	x	x	o	b	b	b	b	o	positive
x	x	x	b	x	o	o	o	b	positive
x	x	x	b	x	o	o	b	o	positive
x	x	x	b	x	o	b	o	o	positive
x	x	x	b	o	x	o	o	b	positive
x	x	x	b	o	x	o	b	o	positive
x	x	x	b	o	x	b	o	o	positive
x	x	x	b	o	o	x	o	b	positive
x	x	x	b	o	o	x	b	o	positive
x	x	x	b	o	o	o	x	b	positive
x	x	x	b	o	o	o	b	x	positive
x	x	x	b	o	o	b	x	o	positive
x	x	x	b	o	o	b	o	x	positive
x	x	x	b	o	o	b	b	b	positive
x	x	x	b	o	b	x	o	o	positive
x	x	x	b	o	b	o	x	o	positive
x	x	x	b	o	b	o	o	x	positive
x	x	x	b	o	b	o	b	b	positive
x	x	x	b	o	b	b	o	b	positive
x	x	x	b	o	b	b	b	o	positive
x	x	x	b	b	o	x	o	o	positive
x	x	x	b	b	o	o	x	o	positive
x	x	x	b	b	o	o	o	x	positive
x	x	x	b	b	o	o	b	b	positive
x	x	x	b	b	o	b	o	b	positive
x	x	x	b	b	o	b	b	o	positive
x	x	x	b	b	b	o	o	b	positive
x	x	x	b	b	b	o	b	o	positive
x	x	x	b	b	b	b	o	o	positive
x	x	o	x	x	o	o	o	x	positive
x	x	o	x	o	x	x	o	o	positive
x	x	o	x	o	o	x	o	x	positive
x	x	o	x	o	o	x	b	b	positive
x	x	o	x	o	b	x	o	b	positive
x	x	o	x	o	b	x	b	o	positive
x	x	o	x	b	o	x	o	b	positive
x	x	o	x	b	b	x	o	o	positive
x	x	o	o	x	x	o	x	o	positive
x	x	o	o	x	x	o	o	x	positive
x	x	o	o	x	o	x	o	x	positive
x	x	o	o	x	o	o	x	x	positive
x	x	o	o	x	o	b	x	b	positive
x	x	o	o	x	o	b	b	x	positive
x	x	o	o	x	b	o	x	b	positive
x	x	o	o	x	b	o	b	x	positive
x	x	o	o	x	b	b	x	o	positive
x	x	o	o	x	b	b	o	x	positive
x	x	o	b	x	o	o	x	b	positive
x	x	o	b	x	o	o	b	x	positive
x	x	o	b	x	o	b	o	x	positive
x	x	o	b	x	b	o	x	o	positive
x	x	o	b	x	b	o	o	x	positive
x	x	b	x	o	o	x	o	b	positive
x	x	b	x	o	o	x	b	o	positive
x	x	b	x	o	b	x	o	o	positive
x	x	b	x	b	o	x	o	o	positive
x	x	b	o	x	o	o	x	b	positive
x	x	b	o	x	o	o	b	x	positive
x	x	b	o	x	o	b	x	o	positive
x	x	b	o	x	o	b	o	x	positive
x	x	b	o	x	b	o	x	o	positive
x	x	b	o	x	b	o	o	x	positive
x	x	b	b	x	o	o	x	o	positive
x	x	b	b	x	o	o	o	x	positive
x	o	x	x	x	o	x	o	o	positive
x	o	x	x	x	o	o	o	x	positive
x	o	x	x	o	o	x	x	o	positive
x	o	x	x	o	o	x	b	b	positive
x	o	x	x	o	b	x	b	o	positive
x	o	x	x	b	o	x	o	b	positive
x	o	x	x	b	o	x	b	o	positive
x	o	x	x	b	b	x	o	o	positive
x	o	x	o	x	x	x	o	o	positive
x	o	x	o	x	x	o	o	x	positive
x	o	x	o	x	o	x	x	o	positive
x	o	x	o	x	o	x	o	x	positive
x	o	x	o	x	o	x	b	b	positive
x	o	x	o	x	o	o	x	x	positive
x	o	x	o	x	o	b	b	x	positive
x	o	x	o	x	b	x	o	b	positive
x	o	x	o	x	b	x	b	o	positive
x	o	x	o	x	b	o	b	x	positive
x	o	x	o	x	b	b	o	x	positive
x	o	x	o	o	x	o	x	x	positive
x	o	x	o	o	x	b	b	x	positive
x	o	x	o	b	x	o	b	x	positive
x	o	x	o	b	x	b	o	x	positive
x	o	x	b	x	o	x	o	b	positive
x	o	x	b	x	o	x	b	o	positive
x	o	x	b	x	o	o	b	x	positive
x	o	x	b	x	o	b	o	x	positive
x	o	x	b	x	b	x	o	o	positive
x	o	x	b	x	b	o	o	x	positive
x	o	x	b	o	x	o	b	x	positive
x	o	x	b	b	x	o	o	x	positive
x	o	o	x	x	x	x	o	o	positive
x	o	o	x	x	x	o	x	o	positive
x	o	o	x	x	x	o	o	x	positive
x	o	o	x	x	x	o	b	b	positive
x	o	o	x	x	x	b	o	b	positive
x	o	o	x	x	x	b	b	o	positive
x	o	o	x	x	o	x	o	x	positive
x	o	o	x	x	o	x	b	b	positive
x	o	o	x	x	o	o	x	x	positive
x	o	o	x	x	o	b	b	x	positive
x	o	o	x	x	b	x	o	b	positive
x	o	o	x	x	b	x	b	o	positive
x	o	o	x	x	b	o	b	x	positive
x	o	o	x	x	b	b	o	x	positive
x	o	o	x	o	x	x	x	o	positive
x	o	o	x	o	x	x	b	b	positive
x	o	o	x	o	o	x	x	x	positive
x	o	o	x	o	b	x	x	b	positive
x	o	o	x	o	b	x	b	x	positive
x	o	o	x	b	x	x	o	b	positive
x	o	o	x	b	x	x	b	o	positive
x	o	o	x	b	o	x	x	b	positive
x	o	o	x	b	o	x	b	x	positive
x	o	o	x	b	b	x	x	o	positive
x	o	o	x	b	b	x	o	x	positive
x	o	o	x	b	b	x	b	b	positive
x	o	o	o	x	x	x	o	x	positive
x	o	o	o	x	x	o	x	x	positive
x	o	o	o	x	x	b	b	x	positive
x	o	o	o	x	o	x	x	x	positive
x	o	o	o	x	b	x	b	x	positive
x	o	o	o	x	b	b	x	x	positive
x	o	o	o	o	x	x	x	x	positive
x	o	o	o	b	b	x	x	x	positive
x	o	o	b	x	x	o	b	x	positive
x	o	o	b	x	x	b	o	x	positive
x	o	o	b	x	o	x	b	x	positive
x	o	o	b	x	o	b	x	x	positive
x	o	o	b	x	b	x	o	x	positive
x	o	o	b	x	b	o	x	x	positive
x	o	o	b	x	b	b	b	x	positive
x	o	o	b	o	b	x	x	x	positive
x	o	o	b	b	o	x	x	x	positive
x	o	b	x	x	x	o	o	b	positive
x	o	b	x	x	x	o	b	o	positive
x	o	b	x	x	x	b	o	o	positive
x	o	b	x	x	o	x	o	b	positive
x	o	b	x	x	o	x	b	o	positive
x	o	b	x	x	o	o	b	x	positive
x	o	b	x	x	o	b	o	x	positive
x	o	b	x	x	b	x	o	o	positive
x	o	b	x	x	b	o	o	x	positive
x	o	b	x	o	x	x	b	o	positive
x	o	b	x	o	o	x	x	b	positive
x	o	b	x	o	o	x	b	x	positive
x	o	b	x	o	b	x	x	o	positive
x	o	b	x	o	b	x	b	b	positive
x	o	b	x	b	x	x	o	o	positive
x	o	b	x	b	o	x	x	o	positive
x	o	b	x	b	o	x	o	x	positive
x	o	b	x	b	o	x	b	b	positive
x	o	b	x	b	b	x	o	b	positive
x	o	b	x	b	b	x	b	o	positive
x	o	b	o	x	x	o	b	x	positive
x	o	b	o	x	x	b	o	x	positive
x	o	b	o	x	o	x	b	x	positive
x	o	b	o	x	o	b	x	x	positive
x	o	b	o	x	b	x	o	x	positive
x	o	b	o	x	b	o	x	x	positive
x	o	b	o	x	b	b	b	x	positive
x	o	b	o	o	b	x	x	x	positive
x	o	b	o	b	o	x	x	x	positive
x	o	b	b	x	x	o	o	x	positive
x	o	b	b	x	o	x	o	x	positive
x	o	b	b	x	o	o	x	x	positive
x	o	b	b	x	o	b	b	x	positive
x	o	b	b	x	b	o	b	x	positive
x	o	b	b	x	b	b	o	x	positive
x	o	b	b	o	o	x	x	x	positive
x	b	x	x	o	o	x	o	b	positive
x	b	x	x	o	o	x	b	o	positive
x	b	x	x	o	b	x	o	o	positive
x	b	x	x	b	o	x	o	o	positive
x	b	x	o	x	o	x	o	b	positive
x	b	x	o	x	o	x	b	o	positive
x	b	x	o	x	o	o	b	x	positive
x	b	x	o	x	o	b	o	x	positive
x	b	x	o	x	b	x	o	o	positive
x	b	x	o	x	b	o	o	x	positive
x	b	x	o	o	x	o	b	x	positive
x	b	x	o	o	x	b	o	x	positive
x	b	x	o	b	x	o	o	x	positive
x	b	x	b	x	o	x	o	o	positive
x	b	x	b	x	o	o	o	x	positive
x	b	x	b	o	x	o	o	x	positive
x	b	o	x	x	x	o	o	b	positive
x	b	o	x	x	x	o	b	o	positive
x	b	o	x	x	x	b	o	o	positive
x	b	o	x	x	o	x	o	b	positive
x	b	o	x	x	o	o	b	x	positive
x	b	o	x	x	o	b	o	x	positive
x	b	o	x	x	b	x	o	o	positive
x	b	o	x	x	b	o	o	x	positive
x	b	o	x	o	x	x	o	b	positive
x	b	o	x	o	x	x	b	o	positive
x	b	o	x	o	o	x	x	b	positive
x	b	o	x	o	o	x	b	x	positive
x	b	o	x	o	b	x	x	o	positive
x	b	o	x	o	b	x	o	x	positive
x	b	o	x	o	b	x	b	b	positive
x	b	o	x	b	x	x	o	o	positive
x	b	o	x	b	o	x	o	x	positive
x	b	o	x	b	o	x	b	b	positive
x	b	o	x	b	b	x	o	b	positive
x	b	o	x	b	b	x	b	o	positive
x	b	o	o	x	x	o	b	x	positive
x	b	o	o	x	x	b	o	x	positive
x	b	o	o	x	o	x	b	x	positive
x	b	o	o	x	o	b	x	x	positive
x	b	o	o	x	b	x	o	x	positive
x	b	o	o	x	b	o	x	x	positive
x	b	o	o	x	b	b	b	x	positive
x	b	o	o	o	b	x	x	x	positive
x	b	o	o	b	o	x	x	x	positive
x	b	o	b	x	x	o	o	x	positive
x	b	o	b	x	o	x	o	x	positive
x	b	o	b	x	o	o	x	x	positive
x	b	o	b	x	o	b	b	x	positive
x	b	o	b	x	b	o	b	x	positive
x	b	o	b	x	b	b	o	x	positive
x	b	o	b	o	o	x	x	x	positive
x	b	b	x	x	o	x	o	o	positive
x	b	b	x	x	o	o	o	x	positive
x	b	b	x	o	x	x	o	o	positive
x	b	b	x	o	o	x	x	o	positive
x	b	b	x	o	o	x	o	x	positive
x	b	b	x	o	o	x	b	b	positive
x	b	b	x	o	b	x	o	b	positive
x	b	b	x	o	b	x	b	o	positive
x	b	b	x	b	o	x	o	b	positive
x	b	b	x	b	o	x	b	o	positive
x	b	b	x	b	b	x	o	o	positive
x	b	b	o	x	x	o	o	x	positive
x	b	b	o	x	o	x	o	x	positive
x	b	b	o	x	o	o	x	x	positive
x	b	b	o	x	o	b	b	x	positive
x	b	b	o	x	b	o	b	x	positive
x	b	b	o	x	b	b	o	x	positive
x	b	b	b	x	o	o	b	x	positive
x	b	b	b	x	o	b	o	x	positive
x	b	b	b	x	b	o	o	x	positive
o	x	x	x	x	o	x	o	o	positive
o	x	x	x	x	o	o	x	o	positive
o	x	x	x	o	x	o	o	x	positive
o	x	x	o	x	x	x	o	o	positive
o	x	x	o	x	o	x	x	o	positive
o	x	x	o	x	o	x	o	x	positive
o	x	x	o	x	o	x	b	b	positive
o	x	x	o	x	o	b	x	b	positive
o	x	x	o	x	b	x	o	b	positive
o	x	x	o	x	b	x	b	o	positive
o	x	x	o	x	b	b	x	o	positive
o	x	x	o	o	x	x	o	x	positive
o	x	x	o	o	x	b	b	x	positive
o	x	x	o	b	x	b	o	x	positive
o	x	x	b	x	o	x	o	b	positive
o	x	x	b	x	o	x	b	o	positive
o	x	x	b	x	o	o	x	b	positive
o	x	x	b	x	o	b	x	o	positive
o	x	x	b	x	b	x	o	o	positive
o	x	x	b	x	b	o	x	o	positive
o	x	x	b	o	x	o	b	x	positive
o	x	x	b	o	x	b	o	x	positive
o	x	x	b	b	x	o	o	x	positive
o	x	o	x	x	x	x	o	o	positive
o	x	o	x	x	x	o	x	o	positive
o	x	o	x	x	x	o	o	x	positive
o	x	o	x	x	x	o	b	b	positive
o	x	o	x	x	x	b	o	b	positive
o	x	o	x	x	x	b	b	o	positive
o	x	o	x	x	o	o	x	x	positive
o	x	o	x	x	o	b	x	b	positive
o	x	o	x	x	b	o	x	b	positive
o	x	o	x	x	b	b	x	o	positive
o	x	o	x	o	o	x	x	x	positive
o	x	o	o	x	x	x	x	o	positive
o	x	o	o	x	x	b	x	b	positive
o	x	o	o	x	o	x	x	x	positive
o	x	o	o	x	b	x	x	b	positive
o	x	o	o	x	b	b	x	x	positive
o	x	o	o	o	x	x	x	x	positive
o	x	o	o	b	b	x	x	x	positive
o	x	o	b	x	x	o	x	b	positive
o	x	o	b	x	x	b	x	o	positive
o	x	o	b	x	o	x	x	b	positive
o	x	o	b	x	o	b	x	x	positive
o	x	o	b	x	b	x	x	o	positive
o	x	o	b	x	b	o	x	x	positive
o	x	o	b	x	b	b	x	b	positive
o	x	o	b	o	b	x	x	x	positive
o	x	o	b	b	o	x	x	x	positive
o	x	b	x	x	x	o	o	b	positive
o	x	b	x	x	x	o	b	o	positive
o	x	b	x	x	x	b	o	o	positive
o	x	b	x	x	o	o	x	b	positive
o	x	b	x	x	o	b	x	o	positive
o	x	b	x	x	b	o	x	o	positive
o	x	b	o	x	x	b	x	o	positive
o	x	b	o	x	o	x	x	b	positive
o	x	b	o	x	o	b	x	x	positive
o	x	b	o	x	b	x	x	o	positive
o	x	b	o	x	b	b	x	b	positive
o	x	b	o	o	b	x	x	x	positive
o	x	b	o	b	o	x	x	x	positive
o	x	b	b	x	x	o	x	o	positive
o	x	b	b	x	o	x	x	o	positive
o	x	b	b	x	o	o	x	x	positive
o	x	b	b	x	o	b	x	b	positive
o	x	b	b	x	b	o	x	b	positive
o	x	b	b	x	b	b	x	o	positive
o	x	b	b	o	o	x	x	x	positive
o	o	x	x	x	x	x	o	o	positive
o	o	x	x	x	x	o	x	o	positive
o	o	x	x	x	x	o	o	x	positive
o	o	x	x	x	x	o	b	b	positive
o	o	x	x	x	x	b	o	b	positive
o	o	x	x	x	x	b	b	o	positive
o	o	x	x	x	o	x	x	o	positive
o	o	x	x	x	o	x	o	x	positive
o	o	x	x	x	o	x	b	b	positive
o	o	x	x	x	b	x	o	b	positive
o	o	x	x	x	b	x	b	o	positive
o	o	x	x	o	x	o	x	x	positive
o	o	x	x	o	x	b	b	x	positive
o	o	x	x	o	o	x	x	x	positive
o	o	x	x	b	x	o	b	x	positive
o	o	x	x	b	x	b	o	x	positive
o	o	x	o	x	x	x	x	o	positive
o	o	x	o	x	x	x	o	x	positive
o	o	x	o	x	x	x	b	b	positive
o	o	x	o	x	x	b	b	x	positive
o	o	x	o	x	o	x	x	x	positive
o	o	x	o	x	b	x	x	b	positive
o	o	x	o	x	b	x	b	x	positive
o	o	x	o	o	x	x	x	x	positive
o	o	x	o	b	x	x	b	x	positive
o	o	x	o	b	x	b	x	x	positive
o	o	x	o	b	b	x	x	x	positive
o	o	x	b	x	x	x	o	b	positive
o	o	x	b	x	x	x	b	o	positive
o	o	x	b	x	x	o	b	x	positive
o	o	x	b	x	x	b	o	x	positive
o	o	x	b	x	o	x	x	b	positive
o	o	x	b	x	o	x	b	x	positive
o	o	x	b	x	b	x	x	o	positive
o	o	x	b	x	b	x	o	x	positive
o	o	x	b	x	b	x	b	b	positive
o	o	x	b	o	x	x	b	x	positive
o	o	x	b	o	x	b	x	x	positive
o	o	x	b	o	b	x	x	x	positive
o	o	x	b	b	x	x	o	x	positive
o	o	x	b	b	x	o	x	x	positive
o	o	x	b	b	x	b	b	x	positive
o	o	x	b	b	o	x	x	x	positive
o	o	b	x	x	x	x	o	b	positive
o	o	b	x	x	x	x	b	o	positive
o	o	b	x	x	x	o	x	b	positive
o	o	b	x	x	x	o	b	x	positive
o	o	b	x	x	x	b	x	o	positive
o	o	b	x	x	x	b	o	x	positive
o	o	b	x	x	x	b	b	b	positive
o	o	b	x	o	b	x	x	x	positive
o	o	b	x	b	o	x	x	x	positive
o	o	b	o	x	b	x	x	x	positive
o	o	b	o	b	x	x	x	x	positive
o	o	b	b	x	o	x	x	x	positive
o	o	b	b	o	x	x	x	x	positive
o	o	b	b	b	b	x	x	x	positive
o	b	x	x	x	x	o	o	b	positive
o	b	x	x	x	x	o	b	o	positive
o	b	x	x	x	x	b	o	o	positive
o	b	x	x	x	o	x	o	b	positive
o	b	x	x	x	o	x	b	o	positive
o	b	x	x	x	b	x	o	o	positive
o	b	x	x	o	x	o	b	x	positive
o	b	x	x	o	x	b	o	x	positive
o	b	x	x	b	x	o	o	x	positive
o	b	x	o	x	x	x	o	b	positive
o	b	x	o	x	x	x	b	o	positive
o	b	x	o	x	x	b	o	x	positive
o	b	x	o	x	o	x	x	b	positive
o	b	x	o	x	o	x	b	x	positive
o	b	x	o	x	b	x	x	o	positive
o	b	x	o	x	b	x	o	x	positive
o	b	x	o	x	b	x	b	b	positive
o	b	x	o	o	x	x	b	x	positive
o	b	x	o	o	x	b	x	x	positive
o	b	x	o	o	b	x	x	x	positive
o	b	x	o	b	x	x	o	x	positive
o	b	x	o	b	x	b	b	x	positive
o	b	x	o	b	o	x	x	x	positive
o	b	x	b	x	x	x	o	o	positive
o	b	x	b	x	x	o	o	x	positive
o	b	x	b	x	o	x	x	o	positive
o	b	x	b	x	o	x	o	x	positive
o	b	x	b	x	o	x	b	b	positive
o	b	x	b	x	b	x	o	b	positive
o	b	x	b	x	b	x	b	o	positive
o	b	x	b	o	x	x	o	x	positive
o	b	x	b	o	x	o	x	x	positive
o	b	x	b	o	x	b	b	x	positive
o	b	x	b	o	o	x	x	x	positive
o	b	x	b	b	x	o	b	x	positive
o	b	x	b	b	x	b	o	x	positive
o	b	o	x	x	x	x	o	b	positive
o	b	o	x	x	x	x	b	o	positive
o	b	o	x	x	x	o	x	b	positive
o	b	o	x	x	x	o	b	x	positive
o	b	o	x	x	x	b	x	o	positive
o	b	o	x	x	x	b	o	x	positive
o	b	o	x	x	x	b	b	b	positive
o	b	o	x	o	b	x	x	x	positive
o	b	o	x	b	o	x	x	x	positive
o	b	o	o	x	b	x	x	x	positive
o	b	o	o	b	x	x	x	x	positive
o	b	o	b	x	o	x	x	x	positive
o	b	o	b	o	x	x	x	x	positive
o	b	o	b	b	b	x	x	x	positive
o	b	b	x	x	x	x	o	o	positive
o	b	b	x	x	x	o	x	o	positive
o	b	b	x	x	x	o	o	x	positive
o	b	b	x	x	x	o	b	b	positive
o	b	b	x	x	x	b	o	b	positive
o	b	b	x	x	x	b	b	o	positive
o	b	b	x	o	o	x	x	x	positive
o	b	b	o	x	o	x	x	x	positive
o	b	b	o	o	x	x	x	x	positive
o	b	b	o	b	b	x	x	x	positive
o	b	b	b	o	b	x	x	x	positive
o	b	b	b	b	o	x	x	x	positive
b	x	x	o	x	o	x	o	b	positive
b	x	x	o	x	o	x	b	o	positive
b	x	x	o	x	o	o	x	b	positive
b	x	x	o	x	o	b	x	o	positive
b	x	x	o	x	b	x	o	o	positive
b	x	x	o	x	b	o	x	o	positive
b	x	x	o	o	x	o	b	x	positive
b	x	x	o	o	x	b	o	x	positive
b	x	x	o	b	x	o	o	x	positive
b	x	x	b	x	o	x	o	o	positive
b	x	x	b	x	o	o	x	o	positive
b	x	x	b	o	x	o	o	x	positive
b	x	o	x	x	x	o	o	b	positive
b	x	o	x	x	x	o	b	o	positive
b	x	o	x	x	x	b	o	o	positive
b	x	o	x	x	o	o	x	b	positive
b	x	o	x	x	b	o	x	o	positive
b	x	o	o	x	x	o	x	b	positive
b	x	o	o	x	x	b	x	o	positive
b	x	o	o	x	o	x	x	b	positive
b	x	o	o	x	o	b	x	x	positive
b	x	o	o	x	b	x	x	o	positive
b	x	o	o	x	b	o	x	x	positive
b	x	o	o	x	b	b	x	b	positive
b	x	o	o	o	b	x	x	x	positive
b	x	o	o	b	o	x	x	x	positive
b	x	o	b	x	x	o	x	o	positive
b	x	o	b	x	o	o	x	x	positive
b	x	o	b	x	o	b	x	b	positive
b	x	o	b	x	b	o	x	b	positive
b	x	o	b	x	b	b	x	o	positive
b	x	o	b	o	o	x	x	x	positive
b	x	b	x	x	o	o	x	o	positive
b	x	b	o	x	x	o	x	o	positive
b	x	b	o	x	o	x	x	o	positive
b	x	b	o	x	o	o	x	x	positive
b	x	b	o	x	o	b	x	b	positive
b	x	b	o	x	b	o	x	b	positive
b	x	b	o	x	b	b	x	o	positive
b	x	b	b	x	o	o	x	b	positive
b	x	b	b	x	o	b	x	o	positive
b	x	b	b	x	b	o	x	o	positive
b	o	x	x	x	x	o	o	b	positive
b	o	x	x	x	x	o	b	o	positive
b	o	x	x	x	x	b	o	o	positive
b	o	x	x	x	o	x	o	b	positive
b	o	x	x	x	o	x	b	o	positive
b	o	x	x	x	b	x	o	o	positive
b	o	x	x	o	x	o	b	x	positive
b	o	x	x	b	x	o	o	x	positive
b	o	x	o	x	x	x	o	b	positive
b	o	x	o	x	x	x	b	o	positive
b	o	x	o	x	x	o	b	x	positive
b	o	x	o	x	x	b	o	x	positive
b	o	x	o	x	o	x	x	b	positive
b	o	x	o	x	o	x	b	x	positive
b	o	x	o	x	b	x	x	o	positive
b	o	x	o	x	b	x	o	x	positive
b	o	x	o	x	b	x	b	b	positive
b	o	x	o	o	x	x	b	x	positive
b	o	x	o	o	x	b	x	x	positive
b	o	x	o	o	b	x	x	x	positive
b	o	x	o	b	x	x	o	x	positive
b	o	x	o	b	x	o	x	x	positive
b	o	x	o	b	x	b	b	x	positive
b	o	x	o	b	o	x	x	x	positive
b	o	x	b	x	x	x	o	o	positive
b	o	x	b	x	x	o	o	x	positive
b	o	x	b	x	o	x	x	o	positive
b	o	x	b	x	o	x	o	x	positive
b	o	x	b	x	o	x	b	b	positive
b	o	x	b	x	b	x	o	b	positive
b	o	x	b	x	b	x	b	o	positive
b	o	x	b	o	x	o	x	x	positive
b	o	x	b	o	x	b	b	x	positive
b	o	x	b	o	o	x	x	x	positive
b	o	x	b	b	x	o	b	x	positive
b	o	x	b	b	x	b	o	x	positive
b	o	o	x	x	x	x	o	b	positive
b	o	o	x	x	x	x	b	o	positive
b	o	o	x	x	x	o	x	b	positive
b	o	o	x	x	x	o	b	x	positive
b	o	o	x	x	x	b	x	o	positive
b	o	o	x	x	x	b	o	x	positive
b	o	o	x	x	x	b	b	b	positive
b	o	o	x	o	b	x	x	x	positive
b	o	o	x	b	o	x	x	x	positive
b	o	o	o	x	b	x	x	x	positive
b	o	o	o	b	x	x	x	x	positive
b	o	o	b	x	o	x	x	x	positive
b	o	o	b	o	x	x	x	x	positive
b	o	o	b	b	b	x	x	x	positive
b	o	b	x	x	x	x	o	o	positive
b	o	b	x	x	x	o	x	o	positive
b	o	b	x	x	x	o	o	x	positive
b	o	b	x	x	x	o	b	b	positive
b	o	b	x	x	x	b	o	b	positive
b	o	b	x	x	x	b	b	o	positive
b	o	b	x	o	o	x	x	x	positive
b	o	b	o	x	o	x	x	x	positive
b	o	b	o	o	x	x	x	x	positive
b	o	b	o	b	b	x	x	x	positive
b	o	b	b	o	b	x	x	x	positive
b	o	b	b	b	o	x	x	x	positive
b	b	x	x	x	o	x	o	o	positive
b	b	x	x	o	x	o	o	x	positive
b	b	x	o	x	x	x	o	o	positive
b	b	x	o	x	x	o	o	x	positive
b	b	x	o	x	o	x	x	o	positive
b	b	x	o	x	o	x	o	x	positive
b	b	x	o	x	o	x	b	b	positive
b	b	x	o	x	b	x	o	b	positive
b	b	x	o	x	b	x	b	o	positive
b	b	x	o	o	x	x	o	x	positive
b	b	x	o	o	x	o	x	x	positive
b	b	x	o	o	x	b	b	x	positive
b	b	x	o	b	x	o	b	x	positive
b	b	x	o	b	x	b	o	x	positive
b	b	x	b	x	o	x	o	b	positive
b	b	x	b	x	o	x	b	o	positive
b	b	x	b	x	b	x	o	o	positive
b	b	x	b	o	x	o	b	x	positive
b	b	x	b	o	x	b	o	x	positive
b	b	x	b	b	x	o	o	x	positive
b	b	o	x	x	x	x	o	o	positive
b	b	o	x	x	x	o	x	o	positive
b	b	o	x	x	x	o	o	x	positive
b	b	o	x	x	x	o	b	b	positive
b	b	o	x	x	x	b	o	b	positive
b	b	o	x	x	x	b	b	o	positive
b	b	o	x	o	o	x	x	x	positive
b	b	o	o	x	o	x	x	x	positive
b	b	o	o	o	x	x	x	x	positive
b	b	o	o	b	b	x	x	x	positive
b	b	o	b	o	b	x	x	x	positive
b	b	o	b	b	o	x	x	x	positive
b	b	b	x	x	x	o	o	b	positive
b	b	b	x	x	x	o	b	o	positive
b	b	b	x	x	x	b	o	o	positive
b	b	b	o	o	b	x	x	x	positive
b	b	b	o	b	o	x	x	x	positive
b	b	b	b	o	o	x	x	x	positive
x	x	o	x	x	o	o	b	o	negative
x	x	o	x	x	o	b	o	o	negative
x	x	o	x	x	b	o	o	o	negative
x	x	o	x	o	x	o	o	b	negative
x	x	o	x	o	x	o	b	o	negative
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b	b	o	x	x	o	x	b	o	negative
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x	o	x	x	o	x	o	x	o	negative
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x	o	x	o	x	x	o	x	o	negative
x	o	x	o	o	x	x	x	o	negative
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o	x	o	x	x	o	x	o	x	negative
o	x	o	x	o	x	x	o	x	negative
o	x	o	o	x	x	x	o	x	negative
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Data_Files_Arff/titanic.arff

Data_Files_Arff/weather.nominal.arff

@relation weather.symbolic @attribute outlook {sunny, overcast, rainy} @attribute temperature {hot, mild, cool} @attribute humidity {high, normal} @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,hot,high,FALSE,no sunny,hot,high,TRUE,no overcast,hot,high,FALSE,yes rainy,mild,high,FALSE,yes rainy,cool,normal,FALSE,yes rainy,cool,normal,TRUE,no overcast,cool,normal,TRUE,yes sunny,mild,high,FALSE,no sunny,cool,normal,FALSE,yes rainy,mild,normal,FALSE,yes sunny,mild,normal,TRUE,yes overcast,mild,high,TRUE,yes overcast,hot,normal,FALSE,yes rainy,mild,high,TRUE,no