IT project

Euty

sharda_dss10e_pp_ch05_NEW.pdf

Home >Computer Science homework help >IT project

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Chapter 5:

Data Mining

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Learning Objectives

 Define data mining as an enabling technology for business intelligence

 Understand the objectives and benefits of business analytics and data mining

 Recognize the wide range of applications of data mining

 Learn the standardized data mining processes

 CRISP-DM

 SEMMA

 KDD (Continued…)

Learning Objectives

 Understand the steps involved in data preprocessing for data mining

 Learn different methods and algorithms of data mining

 Build awareness of the existing data mining software tools

 Commercial versus free/open source

 Understand the pitfalls and myths of data mining

Opening Vignette…

Cabela’s Reels in More Customers with Advanced Analytics and Data Mining

 Decision situation

 Problem

 Proposed solution

 Results

 Answer & discuss the case questions.

Questions for the Opening Vignette

1. Why should retailers, especially omni-channel retailers, pay extra attention to advanced analytics and data mining?

2. What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges?

3. What are the sources of data that retailers such as Cabela’s use for their data mining projects?

4. What does it mean to have a “single view of the customer”? How can it be accomplished?

Data Mining Concepts/Definitions Why Data Mining?

 More intense competition at the global scale.

 Recognition of the value in data sources.

 Availability of quality data on customers, vendors, transactions, Web, etc.

 Consolidation and integration of data repositories into data warehouses.

 The exponential increase in data processing and storage capabilities; and decrease in cost.

 Movement toward conversion of information resources into nonphysical form.

Definition of Data Mining

 The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. - Fayyad et al., (1996)

 Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable.

 Data mining: a misnomer?

 Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…

S ta

tis tic

Management Science &

Information Systems

A rtificia

l In te

llig e n ce

Databases

Pattern

Recognition

Machine

Learning

Mathematical

Modeling

DATA

MINING

Data Mining is at the Intersection of Many Disciplines

 Source of data for DM is often a consolidated data warehouse (not always!).

 DM environment is usually a client-server or a Web-based information systems architecture.

 Data is the most critical ingredient for DM which may include soft/unstructured data.

 The miner is often an end user

 Striking it rich requires creative thinking

 Data mining tools’ capabilities and ease of use are essential (Web, Parallel processing, etc.)

Data Mining Characteristics/Objectives

Application Case 5.1

Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics

Questions For Discussion 1. How did Infinity P&C improve customer service

with data mining?

2. What were the challenges, the proposed solution, and the obtained results?

3. What was their implementation strategy? Why is it important to produce results as early as possible in data mining studies?

Data in Data Mining

 Data: a collection of facts usually obtained as the result of experiences, observations, or experiments.

 Data may consist of numbers, words, images, …

 Data: lowest level of abstraction (from which information and knowledge are derived).

Data

Categorical Numerical

Nominal Ordinal Interval Ratio

Structured Unstructured or

Semi-Structured

MultimediaTextual HTML/XML

 DM extract patterns from data

 Pattern? A mathematical (numeric and/or symbolic) relationship among data items

 Types of patterns

 Association

 Prediction

 Cluster (segmentation)

 Sequential (or time series) relationships

What Does DM Do? How Does it Work?

Application Case 5.2

Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources Questions For Discussion

1. How did the Memphis Police Department used data mining to better combat crime?

2. What were the challenges, the proposed solution, and the obtained results?

A Taxonomy for Data Mining Tasks

Data Mining

Prediction

Classification

Regression

Clustering

Association

Link analysis

Sequence analysis

Learning Method Popular Algorithms

Supervised

Unsupervised

Decision trees, ANN/MLP, SVM, Rough

sets, Genetic Algorithms

Linear/Nonlinear Regression, Regression

trees, ANN/MLP, SVM

Expectation Maximization, Apriory

Algorithm, Graph-based Matching

Apriory Algorithm, FP-Growth technique

K-means, ANN/SOM

Outlier analysis Unsupervised K-means, Expectation Maximization (EM)

Apriory, OneR, ZeroR, Eclat

Classification and Regression Trees,

ANN, SVM, Genetic Algorithms

Data Mining Tasks (cont.)

 Time-series forecasting

 Part of sequence or link analysis?

 Visualization

 Another data mining task?

 Types of DM

 Hypothesis-driven data mining

 Discovery-driven data mining

Data Mining Applications

 Customer Relationship Management

 Maximize return on marketing campaigns

 Improve customer retention (churn analysis)

 Maximize customer value (cross-, up-selling)

 Identify and treat most valued customers

 Banking & Other Financial

 Automate the loan application process

 Detecting fraudulent transactions

 Maximize customer value (cross-, up-selling)

 Optimizing cash reserves with forecasting

Data Mining Applications (cont.)

 Retailing and Logistics

 Optimize inventory levels at different locations

 Improve the store layout and sales promotions

 Optimize logistics by predicting seasonal effects

 Minimize losses due to limited shelf life

 Manufacturing and Maintenance

 Predict/prevent machinery failures

 Identify anomalies in production systems to optimize the use manufacturing capacity

 Discover novel patterns to improve product quality

Data Mining Applications (cont.)

 Brokerage and Securities Trading

 Predict changes on certain bond prices

 Forecast the direction of stock fluctuations

 Assess the effect of events on market movements

 Identify and prevent fraudulent activities in trading

 Insurance

 Forecast claim costs for better business planning

 Determine optimal rate plans

 Optimize marketing to specific customers

 Identify and prevent fraudulent claim activities

Data Mining Applications (cont.)

 Computer hardware and software

 Science and engineering

 Government and defense

 Homeland security and law enforcement

 Travel industry

 Healthcare

 Medicine

 Entertainment industry

 Sports

 Etc.

Increasingly more popular application areas for data mining

Application Case 5.3

A Mine on Terrorist Funding

Questions For Discussion

1. How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case.

2. Do you think data mining, while essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy?

Data Mining Process

 A manifestation of best practices

 A systematic way to conduct DM projects

 Different groups has different versions

 Most common standard processes:

 CRISP-DM (Cross-Industry Standard Process for Data Mining)

 SEMMA (Sample, Explore, Modify, Model, and Assess)

 KDD (Knowledge Discovery in Databases)

Data Mining Process

Source: KDNuggets.com

Data Mining Process: CRISP-DM

Data Sources

Business

Understanding

Data

Preparation

Model

Building

Testing and

Evaluation

Deployment

Data

Understanding

1 2

Data Mining Process: CRISP-DM

Step 1: Business Understanding

Step 2: Data Understanding

Step 3: Data Preparation (!)

Step 4: Model Building

Step 5: Testing and Evaluation

Step 6: Deployment

 The process is highly repetitive and experimental (DM: art versus science?)

Accounts for ~85% of total project time

Data Preparation – A Critical DM Task

Data Consolidation

Data Cleaning

Data Transformation

Data Reduction

Well-formed

Data

Real-world

Data

· Collect data

· Select data

· Integrate data

· Impute missing values

· Reduce noise in data

· Eliminate inconsistencies

· Normalize data

· Discretize/aggregate data

· Construct new attributes

· Reduce number of variables

· Reduce number of cases

· Balance skewed data

Data Mining Process: SEMMA

Sample

(Generate a representative

sample of the data)

Modify (Select variables, transform

variable representations)

Explore (Visualization and basic

description of the data)

Model (Use variety of statistical and

machine learning models )

Assess (Evaluate the accuracy and

usefulness of the models)

SEMMA

Application Case 5.4

Data Mining in Cancer Research

Questions For Discussion

1. How can data mining be used for ultimately curing illnesses like cancer?

2. What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors?

Data Mining Methods: Classification

 Most frequently used DM method

 Part of the machine-learning family

 Employ supervised learning

 Learn from past data, classify new data

 The output variable is categorical (nominal or ordinal) in nature

 Classification versus regression?

 Classification versus clustering?

 Predictive accuracy

 Hit rate

 Speed

 Model building; predicting

 Robustness

 Scalability

 Interpretability

 Transparency, explainability

Assessment Methods for Classification

Accuracy of Classification Models

 In classification problems, the primary source for accuracy estimation is the confusion matrix

True

Positive

Count (TP)

False

Positive

Count (FP)

True

Negative

Count (TN)

False

Negative

Count (FN)

True Class

Positive Negative

P o

s it iv

e N

e g

a ti v e

P re

d ic

te d

C la

s s

FNTP

TP RatePositiveTrue

 

FPTN

TN RateNegativeTrue

 

FNFPTNTP

TNTP Accuracy



 

FPTP

TP recision

 P

FNTP

TP callRe

 

Estimation Methodologies for Classification

 Simple split (or holdout or test sample estimation)

 Split the data into 2 mutually exclusive sets training (~70%) and testing (30%)

 For ANN, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%])

Preprocessed

Data

Training Data

Testing Data

Model

Development

Model

Assessment

(scoring)

2/3

1/3

Classifier

Prediction

Accuracy

Estimation Methodologies for Classification

 k-Fold Cross Validation (rotation estimation)  Split the data into k mutually exclusive subsets

 Use each subset as testing while using the rest of the subsets as training

 Repeat the experimentation for k times

 Aggregate the test results for true estimation of prediction accuracy training

 Other estimation methodologies

 Leave-one-out, bootstrapping, jackknifing

 Area under the ROC curve

Estimation Methodologies for Classification – ROC Curve

10.90.80.70.60.50.40.30.20.10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9

0.8

False Positive Rate (1 - Specificity)

T ru

e P

o si

tiv e

R a

te (

S e

n si

tiv ity

) A

Classification Techniques

 Decision tree analysis

 Statistical analysis

 Neural networks

 Support vector machines

 Case-based reasoning

 Bayesian classifiers

 Genetic algorithms

 Rough sets

Decision Trees

1. Create a root node and assign all of the training data to it.

2. Select the best splitting attribute.

3. Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split.

4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached.

A general algorithm for decision tree building

 Employs the divide and conquer method

 Recursively divides a training set until each division consists of examples from one class

Decision Trees

 DT algorithms mainly differ on

1. Splitting criteria

 Which variable, what value, etc.

2. Stopping criteria

 When to stop building the tree

3. Pruning (generalization method)

 Pre-pruning versus post-pruning

 Most popular DT algorithms include

 ID3, C4.5, C5; CART; CHAID; M5

Decision Trees

 Alternative splitting criteria

 Gini index determines the purity of a specific class as a result of a decision to branch along a particular attribute/value

 Used in CART

 Information gain uses entropy to measure the extent of uncertainty or randomness of a particular attribute/value split

 Used in ID3, C4.5, C5

 Chi-square statistics (used in CHAID)

Application Case 5.5

2degrees Gets a 1275 Percent Boost in Churn Identification Questions For Discussion 1. What does 2degrees do? Why is it important for

2degrees to accurately identify churn? 2. What were the challenges, the proposed solution,

and the obtained results? 3. How can data mining help in identifying customer

churn? How do some companies do it without using data mining tools and techniques?

4. Why is it important for Delta Lloyd Group to comply with industry regulations?

Cluster Analysis for Data Mining

 Used for automatic identification of natural groupings of things

 Part of the machine-learning family

 Employ unsupervised learning

 Learns the clusters of things from past data, then assigns new instances

 There is not an output variable

 Also known as segmentation

Cluster Analysis for Data Mining

 Clustering results may be used to  Identify natural groupings of customers

 Identify rules for assigning new cases to classes for targeting/diagnostic purposes

 Provide characterization, definition, labeling of populations

 Decrease the size and complexity of problems for other data mining methods

 Identify outliers in a specific domain (e.g., rare-event detection)

Cluster Analysis for Data Mining

 Analysis methods

 Statistical methods (including both hierarchical and nonhierarchical), such as k- means, k-modes, and so on.

 Neural networks (adaptive resonance theory [ART], self-organizing map [SOM])

 Fuzzy logic (e.g., fuzzy c-means algorithm)

 Genetic algorithms

Cluster Analysis for Data Mining

 How many clusters?

 There is not a “truly optimal” way to calculate it

 Heuristics are often used

 Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items.

 Euclidian versus Manhattan/Rectilinear distance

Cluster Analysis for Data Mining

 k-Means Clustering Algorithm

 k : pre-determined number of clusters

 Algorithm (Step 0: determine value of k)

Step 1: Randomly generate k random points as initial cluster centers.

Step 2: Assign each point to the nearest cluster center.

Step 3: Re-compute the new cluster centers.

Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable).

Cluster Analysis for Data Mining - k-Means Clustering Algorithm

Step 1 Step 2 Step 3

Association Rule Mining

 A very popular DM method in business

 Finds interesting relationships (affinities) between variables (items or events)

 Part of machine learning family

 Employs unsupervised learning

 There is no output variable

 Also known as market basket analysis

 Often used as an example to describe DM to ordinary people, such as the famous “relationship between diapers and beers!”

Association Rule Mining

 Input: the simple point-of-sale transaction data

 Output: Most frequent affinities among items

 Example: according to the transaction data…

“Customer who bought a lap-top computer and a virus protection software, also bought extended service plan 70 percent of the time."

 How do you use such a pattern/knowledge?

 Put the items next to each other

 Promote the items as a package

 Place items far apart from each other!

Association Rule Mining

 A representative applications of association rule mining include

 In business: cross-marketing, cross-selling, store design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration

 In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical DSS); and genes and their functions (to be used in genomics projects)

 …

Association Rule Mining

 Are all association rules interesting and useful?

A Generic Rule: X  Y [S%, C%]

X, Y: products and/or services

X: Left-hand-side (LHS)

Y: Right-hand-side (RHS)

S: Support: how often X and Y go together

C: Confidence: how often Y go together with the X

Example: {Laptop Computer, Antivirus Software}  {Extended Service Plan} [30%, 70%]

Association Rule Mining

 Algorithms are available for generating association rules

 Apriori

 Eclat

 FP-Growth

 + Derivatives and hybrids of the three

 The algorithms help identify the frequent item sets, which are, then converted to association rules

Association Rule Mining

 Apriori Algorithm  Finds subsets that are common to at least a

minimum number of the itemsets

 Uses a bottom-up approach  frequent subsets are extended one item at a time

(the size of frequent subsets increases from one- item subsets to two-item subsets, then three-item subsets, and so on), and

 groups of candidates at each level are tested against the data for minimum support.

(see the figure)  --

Association Rule Mining Apriori Algorithm

Itemset

(SKUs) Support

Transaction

SKUs

(Item No)

1, 2, 3, 4

2, 3, 4

2, 3

1, 2, 4

1, 2, 3, 4

2, 4

Raw Transaction Data

Itemset

(SKUs) Support

1, 2

1, 3

1, 4

2, 3

3, 4

2, 4

Itemset

(SKUs) Support

1, 2, 4

2, 3, 4

One-item Itemsets Two-item Itemsets Three-item Itemsets

Data Mining Software

 Commercial

 IBM SPSS Modeler (formerly Clementine)

 SAS - Enterprise Miner

 IBM - Intelligent Miner

 StatSoft – Statistica Data Miner

 … many more

 Free and/or Open Source

 R

 RapidMiner

 Weka…

0 50 100 150 200 250 300

Predixion Software (3)

WordStat (3)

11 Ants Analytics (4)

Teradata Miner (4)

RapidInsight/Veera (5)

Angoss (7)

SAP (BusinessObjects/Sybase/Hana)(7)

XLSTAT (7)

Salford SPM/CART/MARS/TreeNet/RF (9)

Revolution Computing (11)

C4.5/C5.0/See5 (13)

Bayesia (14)

KXEN (14)

Zementis (14)

Stata (15)

IBM Cognos (16)

Miner3D (19)

Mathematica (23)

JMP (32)

Other commercial software (32)

Oracle Data Miner (35)

Tableau (35)

TIBCO Spotfire / S+ / Miner (37)

Other free software (39)

Microsoft SQL Server (40)

Orange (42)

SAS Enterprise Miner (46)

IBM SPSS Modeler (54)

IBM SPSS Statistics (62)

MATLAB (80)

Rapid-I RapidAnalytics (83)

SAS (101)

StatSoft Statistica (112)

Weka / Pentaho (118)

KNIME (174)

Rapid-I RapidMiner (213)

Excel (238)

R (245)

Source: KDNuggets.com

Big Data Software Tools and Platforms

0 10 20 30 40 50 60 70 80

Other Hadoop-based tools (10)

Other Big Data software (21)

NoSQL databases (33)

Amazon Web Services (AWS) (36)

Apache Hadoop/Hbase/Pig/Hive (67)

0 50 100 150 200 250 300

F# (5)

Awk/Gawk/Shell (31)

Perl (37)

Other languages (57)

C/C++ (66)

Python (119)

Java (138)

SQL (185)

R (245)

Application Case 5.6

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Questions For Discussion

 Decision situation

 Problem

 Proposed solution

 Results

 Answer & discuss the case questions.

Application Case 5.6 Data Mining Goes to Hollywood!

Independent Variable Number of

Values Possible Values

MPAA Rating 5 G, PG, PG-13, R, NR

Competition 3 High, Medium, Low

Star value 3 High, Medium, Low

Genre 10

Sci-Fi, Historic Epic Drama,

Modern Drama, Politically

Related, Thriller, Horror,

Comedy, Cartoon, Action,

Documentary

Special effects 3 High, Medium, Low

Sequel 1 Yes, No

Number of screens 1 Positive integer

Class No. 1 2 3 4 5 6 7 8 9

Range

(in $Millions)

< 1

(Flop)

> 1

< 10

> 10

< 20

> 20

< 40

> 40

< 65

> 65

< 100

> 100

< 150

> 150

< 200

> 200

(Blockbuster)

Dependent Variable

Independent Variables

A Typical Classification Problem

Application Case 5.6 Data Mining Goes to Hollywood!

Model

Development

process

Model

Assessment

process

The DM Process Map in IBM SPSS Modeler

Application Case 5.6 Data Mining Goes to Hollywood!

Prediction Models

Individual Models Ensemble Models

Performance

Measure SVM ANN C&RT

Random

Forest

Boosted

Tree

Fusion

(Average)

Count (Bingo) 192 182 140 189 187 194

Count (1-Away) 104 120 126 121 104 120

Accuracy (% Bingo) 55.49% 52.60% 40.46% 54.62% 54.05% 56.07%

Accuracy (% 1-Away) 85.55% 87.28% 76.88% 89.60% 84.10% 90.75%

Standard deviation 0.93 0.87 1.05 0.76 0.84 0.63

* Training set: 1998 – 2005 movies; Test set: 2006 movies

Application Case 5.7 Data Mining & Privacy Issues

Predicting Customer Buying Patterns— The Target Story

Questions For Discussion 1. What do you think about data mining and its

implication for privacy? What is the threshold between discovery of knowledge and infringement of privacy?

2. Did Target go too far? Did it do anything illegal? What do you think Target should have done? What do you think Target should do next (quit these types of practices)?

Data Mining Myths

 Data mining …

 provides instant solutions/predictions

 is not yet viable for business applications

 requires a separate, dedicated database

 can only be done by those with advanced degrees

 is only for large firms that have lots of customer data

 is another name for the good-old statistics

Common Data Mining Blunders

1. Selecting the wrong problem for data mining

2. Ignoring what your sponsor thinks data mining is and what it really can/cannot do

3. Not leaving insufficient time for data acquisition, selection and preparation

4. Looking only at aggregated results and not at individual records/predictions

5. Being sloppy about keeping track of the data mining procedure and results

6. …more in the book

End of the Chapter

 Questions, comments

means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the

United States of America.