Discovering Knowledge in Data: An Introduction to Data Mining / Edition 1

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Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.
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Editorial Reviews

From the Publisher
" excellent introductory book of data mining. I recommend it for every one who wants to learn data mining." (Journal of Statistical Software, May 2006)

"...selected material is described in a simple, clear, and…precise studies…examples, and screen shots has definitely added to the learning value of the book." (Journal of Biopharmaceutical Statistics, January/February 2006)

"...does a good job introducing data mining to skillfully previews some of the basic statistical issues needed to understand data mining techniques." (Journal of the American Statistical Association, December 2005)

"If you need a book to help colleagues understand your data mining procedures and results, this is the one you want to give them." (Technometrics, November 2005)

"…an excellent book…it should be useful for anyone interested in analysing epidemiological data." (Statistics in Medical Research, October 2005)

" excellent 'white-box' overview of established approaches for data analysis, in which readers are shown how, why, and when the methods work." (CHOICE, April 2005)

"Larose has the making of a good series of books on data mining…I, for one, look forward to the next two books in the series." (Computing, February 15, 2005)

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Product Details

  • ISBN-13: 9780471666578
  • Publisher: Wiley
  • Publication date: 11/28/2004
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 240
  • Product dimensions: 6.46 (w) x 9.23 (h) x 0.69 (d)

Meet the Author

DANIEL T. LAROSE received his PhD in statistics from the University of Connecticut. An associate professor of statistics at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world's first online master of science program in data mining. He has also worked as a data mining consultant for Connecticut-area companies. He is currently working on the next two books of his three-volume series on Data Mining: Data Mining Methods and Models and Data Mining the Web: Uncovering Patterns in Web Content, scheduled to publish respectively in 2005 and 2006.

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Table of Contents



What Is Data Mining? 2

Why Data Mining? 4

Need for Human Direction of Data Mining 4

Cross-Industry Standard Process: CRISP–DM 5

Case Study 1: Analyzing Automobile Warranty Claims: Example of the CRISP–DM Industry Standard Process in Action 8

Fallacies of Data Mining 10

What Tasks Can Data Mining Accomplish? 11

Description 11

Estimation 12

Prediction 13

Classification 14

Clustering 16

Association 17

Case Study 2: Predicting Abnormal Stock Market Returns Using Neural Networks 18

Case Study 3: Mining Association Rules from Legal Databases 19

Case Study 4: Predicting Corporate Bankruptcies Using Decision Trees 21

Case Study 5: Profiling the Tourism Market Using k-Means Clustering Analysis 23

References 24

Exercises 25


Why Do We Need to Preprocess the Data? 27

Data Cleaning 28

Handling Missing Data 30

Identifying Misclassifications 33

Graphical Methods for Identifying Outliers 34

Data Transformation 35

Min–Max Normalization 36

Z-Score Standardization 37

Numerical Methods for Identifying Outliers 38

References 39

Exercises 39


Hypothesis Testing versus Exploratory Data Analysis 41

Getting to Know the Data Set 42

Dealing with Correlated Variables 44

Exploring Categorical Variables 45

Using EDA to Uncover Anomalous Fields 50

Exploring Numerical Variables 52

Exploring Multivariate Relationships 59

Selecting Interesting Subsets of the Data for Further Investigation 61

Binning 62

Summary 63

References 64

Exercises 64


Data Mining Tasks in Discovering Knowledge in Data 67

Statistical Approaches to Estimation and Prediction 68

Univariate Methods: Measures of Center and Spread 69

Statistical Inference 71

How Confident Are We in Our Estimates? 73

Confidence Interval Estimation 73

Bivariate Methods: Simple Linear Regression 75

Dangers of Extrapolation 79

Confidence Intervals for the Mean Value of y Given x 80

Prediction Intervals for a Randomly Chosen Value of y Given x 80

Multiple Regression 83

Verifying Model Assumptions 85

References 88

Exercises 88


Supervised versus Unsupervised Methods 90

Methodology for Supervised Modeling 91

Bias–Variance Trade-Off 93

Classification Task 95

k-Nearest Neighbor Algorithm 96

Distance Function 99

Combination Function 101

Simple Unweighted Voting 101

Weighted Voting 102

Quantifying Attribute Relevance: Stretching the Axes 103

Database Considerations 104

k-Nearest Neighbor Algorithm for Estimation and Prediction 104

Choosing k 105

Reference 106

Exercises 106


Classification and Regression Trees 109

C4.5 Algorithm 116

Decision Rules 121

Comparison of the C5.0 and CART Algorithms Applied to Real Data 122

References 126

Exercises 126


Input and Output Encoding 129

Neural Networks for Estimation and Prediction 131

Simple Example of a Neural Network 131

Sigmoid Activation Function 134

Back-Propagation 135

Gradient Descent Method 135

Back-Propagation Rules 136

Example of Back-Propagation 137

Termination Criteria 139

Learning Rate 139

Momentum Term 140

Sensitivity Analysis 142

Application of Neural Network Modeling 143

References 145

Exercises 145


Clustering Task 147

Hierarchical Clustering Methods 149

Single-Linkage Clustering 150

Complete-Linkage Clustering 151

k-Means Clustering 153

Example of k-Means Clustering at Work 153

Application of k-Means Clustering Using SAS Enterprise Miner 158

Using Cluster Membership to Predict Churn 161

References 161

Exercises 162


Self-Organizing Maps 163

Kohonen Networks 165

Example of a Kohonen Network Study 166

Cluster Validity 170

Application of Clustering Using Kohonen Networks 170

Interpreting the Clusters 171

Cluster Profiles 175

Using Cluster Membership as Input to Downstream Data Mining Models 177

References 178

Exercises 178


Affinity Analysis and Market Basket Analysis 180

Data Representation for Market Basket Analysis 182

Support, Confidence, Frequent Itemsets, and the A Priori Property 183

How Does the A Priori AlgorithmWork (Part 1)? Generating Frequent Itemsets 185

How Does the A Priori AlgorithmWork (Part 2)? Generating Association Rules 186

Extension from Flag Data to General Categorical Data 189

Information-Theoretic Approach: Generalized Rule Induction Method 190

J-Measure 190

Application of Generalized Rule Induction 191

When Not to Use Association Rules 193

Do Association Rules Represent Supervised or Unsupervised Learning? 196

Local Patterns versus Global Models 197

References 198

Exercises 198


Model Evaluation Techniques for the Description Task 201

Model Evaluation Techniques for the Estimation and Prediction Tasks 201

Model Evaluation Techniques for the Classification Task 203

Error Rate, False Positives, and False Negatives 203

Misclassification Cost Adjustment to Reflect Real-World Concerns 205

Decision Cost/Benefit Analysis 207

Lift Charts and Gains Charts 208

Interweaving Model Evaluation with Model Building 211

Confluence of Results: Applying a Suite of Models 212

Reference 213

Exercises 213



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