Discovering Knowledge in Data: An Introduction to Data Mining

ISBN-13: 9780470908747 / Angielski / Twarda / 2014 / 336 str.

Daniel T. Larose

Opis

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will "learn data mining by doing data mining." By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.

• The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.
• Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization
• Offers extensive coverage of the R statistical programming language
• Contains 280 end-of-chapter exercises
• Includes a companion website for university instructors who adopt the book

Spis treści

PREFACE xi

CHAPTER 1 AN INTRODUCTION TO DATA MINING 1

1.1 What is Data Mining? 1

1.2 Wanted: Data Miners 2

1.3 The Need for Human Direction of Data Mining 3

1.4 The Cross–Industry Standard Practice for Data Mining 4

1.4.1 Crisp–DM: The Six Phases 5

1.5 Fallacies of Data Mining 6

1.6 What Tasks Can Data Mining Accomplish? 8

1.6.1 Description 8

1.6.2 Estimation 8

1.6.3 Prediction 10

1.6.4 Classification 10

1.6.5 Clustering 12

1.6.6 Association 14

References 14

Exercises 15

CHAPTER 2 DATA PREPROCESSING 16

2.1 Why do We Need to Preprocess the Data? 17

2.2 Data Cleaning 17

2.3 Handling Missing Data 19

2.4 Identifying Misclassifications 22

2.5 Graphical Methods for Identifying Outliers 22

2.6 Measures of Center and Spread 23

2.7 Data Transformation 26

2.8 Min–Max Normalization 26

2.9 Z–Score Standardization 27

2.10 Decimal Scaling 28

2.11 Transformations to Achieve Normality 28

2.12 Numerical Methods for Identifying Outliers 35

2.13 Flag Variables 36

2.14 Transforming Categorical Variables into Numerical Variables 37

2.15 Binning Numerical Variables 38

2.16 Reclassifying Categorical Variables 39

2.17 Adding an Index Field 39

2.18 Removing Variables that are Not Useful 39

2.19 Variables that Should Probably Not Be Removed 40

2.20 Removal of Duplicate Records 41

2.21 A Word About ID Fields 41

The R Zone 42

References 48

Exercises 48

Hands–On Analysis 50

CHAPTER 3 EXPLORATORY DATA ANALYSIS 51

3.1 Hypothesis Testing Versus Exploratory Data Analysis 51

3.2 Getting to Know the Data Set 52

3.3 Exploring Categorical Variables 55

3.4 Exploring Numeric Variables 62

3.5 Exploring Multivariate Relationships 69

3.6 Selecting Interesting Subsets of the Data for Further Investigation 71

3.7 Using EDA to Uncover Anomalous Fields 71

3.8 Binning Based on Predictive Value 72

3.9 Deriving New Variables: Flag Variables 74

3.10 Deriving New Variables: Numerical Variables 77

3.11 Using EDA to Investigate Correlated Predictor Variables 77

3.12 Summary 80

The R Zone 82

Reference 88

Exercises 88

Hands–On Analysis 89

CHAPTER 4 UNIVARIATE STATISTICAL ANALYSIS 91

4.1 Data Mining Tasks in Discovering Knowledge in Data 91

4.2 Statistical Approaches to Estimation and Prediction 92

4.3 Statistical Inference 93

4.4 How Confident are We in Our Estimates? 94

4.5 Confidence Interval Estimation of the Mean 95

4.6 How to Reduce the Margin of Error 97

4.7 Confidence Interval Estimation of the Proportion 98

4.8 Hypothesis Testing for the Mean 99

4.9 Assessing the Strength of Evidence Against the Null Hypothesis 101

4.10 Using Confidence Intervals to Perform Hypothesis Tests 102

4.11 Hypothesis Testing for the Proportion 104

The R Zone 105

Reference 106

Exercises 106

CHAPTER 5 MULTIVARIATE STATISTICS 109

5.1 Two–Sample t–Test for Difference in Means 110

5.2 Two–Sample Z–Test for Difference in Proportions 111

5.3 Test for Homogeneity of Proportions 112

5.4 Chi–Square Test for Goodness of Fit of Multinomial Data 114

5.5 Analysis of Variance 115

5.6 Regression Analysis 118

5.7 Hypothesis Testing in Regression 122

5.8 Measuring the Quality of a Regression Model 123

5.9 Dangers of Extrapolation 123

5.10 Confidence Intervals for the Mean Value of y Given x 125

5.11 Prediction Intervals for a Randomly Chosen Value of y Given x 125

5.12 Multiple Regression 126

5.13 Verifying Model Assumptions 127

The R Zone 131

Reference 135

Exercises 135

Hands–On Analysis 136

CHAPTER 6 PREPARING TO MODEL THE DATA 138

6.1 Supervised Versus Unsupervised Methods 138

6.2 Statistical Methodology and Data Mining Methodology 139

6.3 Cross–Validation 139

6.4 Overfitting 141

6.5 BIAS Variance Trade–Off 142

6.6 Balancing the Training Data Set 144

6.7 Establishing Baseline Performance 145

The R Zone 146

Reference 147

Exercises 147

CHAPTER 7 k–NEAREST NEIGHBOR ALGORITHM 149

7.1 Classification Task 149

7.2 k–Nearest Neighbor Algorithm 150

7.3 Distance Function 153

7.4 Combination Function 156

7.4.1 Simple Unweighted Voting 156

7.4.2 Weighted Voting 156

7.5 Quantifying Attribute Relevance: Stretching the Axes 158

7.6 Database Considerations 158

7.7 k–Nearest Neighbor Algorithm for Estimation and Prediction 159

7.8 Choosing k 160

7.9 Application of k–Nearest Neighbor Algorithm Using IBM/SPSS Modeler 160

The R Zone 162

Exercises 163

Hands–On Analysis 164

CHAPTER 8 DECISION TREES 165

8.1 What is a Decision Tree? 165

8.2 Requirements for Using Decision Trees 167

8.3 Classification and Regression Trees 168

8.4 C4.5 Algorithm 174

8.5 Decision Rules 179

8.6 Comparison of the C5.0 and Cart Algorithms Applied to Real Data 180

The R Zone 183

References 184

Exercises 185

Hands–On Analysis 185

CHAPTER 9 NEURAL NETWORKS 187

9.1 Input and Output Encoding 188

9.2 Neural Networks for Estimation and Prediction 190

9.3 Simple Example of a Neural Network 191

9.4 Sigmoid Activation Function 193

9.5 Back–Propagation 194

9.5.1 Gradient Descent Method 194

9.5.2 Back–Propagation Rules 195

9.5.3 Example of Back–Propagation 196

9.6 Termination Criteria 198

9.7 Learning Rate 198

9.8 Momentum Term 199

9.9 Sensitivity Analysis 201

9.10 Application of Neural Network Modeling 202

The R Zone 204

References 207

Exercises 207

Hands–On Analysis 207

CHAPTER 10 HIERARCHICAL AND k–MEANS CLUSTERING 209

10.1 The Clustering Task 209

10.2 Hierarchical Clustering Methods 212

10.3 Single–Linkage Clustering 213

10.4 Complete–Linkage Clustering 214

10.5 k–Means Clustering 215

10.6 Example of k–Means Clustering at Work 216

10.7 Behavior of MSB, MSE, and PSEUDO–F as the k–Means Algorithm Proceeds 219

10.8 Application of k–Means Clustering Using SAS Enterprise Miner 220

10.9 Using Cluster Membership to Predict Churn 223

The R Zone 224

References 226

Exercises 226

Hands–On Analysis 226

CHAPTER 11 KOHONEN NETWORKS 228

11.1 Self–Organizing Maps 228

11.2 Kohonen Networks 230

11.2.1 Kohonen Networks Algorithm 231

11.3 Example of a Kohonen Network Study 231

11.4 Cluster Validity 235

11.5 Application of Clustering Using Kohonen Networks 235

11.6 Interpreting the Clusters 237

11.6.1 Cluster Profiles 240

11.7 Using Cluster Membership as Input to Downstream Data Mining Models 242

The R Zone 243

References 245

Exercises 245

Hands–On Analysis 245

CHAPTER 12 ASSOCIATION RULES 247

12.1 Affinity Analysis and Market Basket Analysis 247

12.1.1 Data Representation for Market Basket Analysis 248

12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property 249

12.3 How Does the a Priori Algorithm Work? 251

12.3.1 Generating Frequent Itemsets 251

12.3.2 Generating Association Rules 253

12.4 Extension from Flag Data to General Categorical Data 255

12.5 Information–Theoretic Approach: Generalized Rule Induction Method 256

12.5.1 J–Measure 257

12.6 Association Rules are Easy to do Badly 258

12.7 How Can We Measure the Usefulness of Association Rules? 259

12.8 Do Association Rules Represent Supervised or Unsupervised Learning? 260

12.9 Local Patterns Versus Global Models 261

The R Zone 262

References 263

Exercises 263

Hands–On Analysis 264

CHAPTER 13 IMPUTATION OF MISSING DATA 266

13.1 Need for Imputation of Missing Data 266

13.2 Imputation of Missing Data: Continuous Variables 267

13.3 Standard Error of the Imputation 270

13.4 Imputation of Missing Data: Categorical Variables 271

13.5 Handling Patterns in Missingness 272

The R Zone 273

Reference 276

Exercises 276

Hands–On Analysis 276

CHAPTER 14 MODEL EVALUATION TECHNIQUES 277

14.1 Model Evaluation Techniques for the Description Task 278

14.2 Model Evaluation Techniques for the Estimation and Prediction Tasks 278

14.3 Model Evaluation Techniques for the Classification Task 280

14.4 Error Rate, False Positives, and False Negatives 280

14.5 Sensitivity and Specificity 283

14.6 Misclassification Cost Adjustment to Reflect Real–World Concerns 284

14.7 Decision Cost/Benefit Analysis 285

14.8 Lift Charts and Gains Charts 286

14.9 Interweaving Model Evaluation with Model Building 289

14.10 Confluence of Results: Applying a Suite of Models 290

The R Zone 291

Reference 291

Exercises 291

Hands–On Analysis 291

APPENDIX: DATA SUMMARIZATION AND VISUALIZATION 294

INDEX 309

O autorze

Daniel T. Larose earned his PhD in Statistics at the University of Connecticut. He is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University.  His consulting clients have included Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. This is Larose s fourth book for Wiley.

Chantal D. Larose is an Assistant Professor of Statistics & Data Science at Eastern Connecticut State University (ECSU).  She has co–authored three books on data science and predictive analytics.  She helped develop data science programs at ECSU and at SUNY New Paltz.  She received her PhD in Statistics from the University of Connecticut, Storrs in 2015 (dissertation title: Model–based Clustering of Incomplete Data).

Blurb

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever–increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.

This book provides the tools needed to thrive in today s big data world. The author demonstrates how to leverage a company s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will learn data mining by doing data mining . By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining .