Research paper
krish1912
DataFiles.zipData 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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2,137,86.6,144.6,63.9,50.8,1713,92,2.91,3.41,9.6,58,4800,49,54,6479 2,137,86.6,144.6,63.9,50.8,1819,92,2.91,3.41,9.2,76,6000,31,38,6855 1,101,93.7,150,64,52.6,1837,79,2.91,3.07,10.1,60,5500,38,42,5399 1,101,93.7,150,64,52.6,1940,92,2.91,3.41,9.2,76,6000,30,34,6529 1,101,93.7,150,64,52.6,1956,92,2.91,3.41,9.2,76,6000,30,34,7129 0,110,96.5,163.4,64,54.5,2010,92,2.91,3.41,9.2,76,6000,30,34,7295 0,78,96.5,157.1,63.9,58.3,2024,92,2.92,3.41,9.2,76,6000,30,34,7295 0,106,96.5,167.5,65.2,53.3,2236,110,3.15,3.58,9,86,5800,27,33,7895 0,106,96.5,167.5,65.2,53.3,2289,110,3.15,3.58,9,86,5800,27,33,9095 0,85,96.5,175.4,65.2,54.1,2304,110,3.15,3.58,9,86,5800,27,33,8845 0,85,96.5,175.4,62.5,54.1,2372,110,3.15,3.58,9,86,5800,27,33,10295 0,85,96.5,175.4,65.2,54.1,2465,110,3.15,3.58,9,101,5800,24,28,12945 1,107,96.5,169.1,66,51,2293,110,3.15,3.58,9.1,100,5500,25,31,10345 0,145,113,199.6,69.6,52.8,4066,258,3.63,4.17,8.1,176,4750,15,19,32250 1,104,93.1,159.1,64.2,54.1,1890,91,3.03,3.15,9,68,5000,30,31,5195 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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 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/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 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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 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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}
