§ North, "Data Mining for the Masses" (2012)

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Table of Contents

SECTION ONE: Data Mining Basics

Chapter One: Introduction to Data Mining and CRISP-DM 3

Introduction 3
A Note About Tools 4
The Data Mining Process 5
Data Mining and You 11

Chapter Two: Organizational Understanding and Data Understanding 13

Context and Perspective 13
Learning Objectives 14
Purposes, Intents and Limitations of Data Mining 15
Database, Data Warehouse, Data Mart, Data Set…? 15
Types of Data 19
A Note about Privacy and Security 20
Chapter Summary 21
Review Questions 22
Exercises 22

Chapter Three: Data Preparation 25

Context and Perspective 25
Learning Objectives 25
Collation 27
Data Scrubbing 28
Hands on Exercise 29
Preparing RapidMiner, Importing Data, and 30
Handling Missing Data 30
Data Reduction 46
Handling Inconsistent Data 50
Attribute Reduction 52
Chapter Summary 54
Review Questions 55
Exercise 55

SECTION TWO: Data Mining Models and Methods 57

Chapter Four: Correlation 59

Context and Perspective 59
Learning Objectives 59
Organizational Understanding 59
Data Understanding 60
Data Preparation 60
Modeling 62
Evaluation 63
Deployment 65
Chapter Summary 67
Review Questions 68
Exercise 68

Chapter Five: Association Rules 73

Context and Perspective 73
Learning Objectives 73
Organizational Understanding 73
Data Understanding 74
Data Preparation 76
Modeling 81
Evaluation 84
Deployment 87
Chapter Summary 87
Review Questions 88
Exercise 88

Chapter Six: k-Means Clustering 91

Context and Perspective 91
Learning Objectives 91
Organizational Understanding 91
Data UnderstanDing 92
Data Preparation 92
Modeling 94
Evaluation 96
Deployment 98
Chapter Summary 101
Review Questions 101
Exercise 102

Chapter Seven: Discriminant Analysis 105

Context and Perspective 105
Learning Objectives 105
Organizational Understanding 106
Data Understanding 106
Data Preparation 109
Modeling 114
Evaluation 118
Deployment 120
Chapter Summary 121
Review Questions 122
Exercise 123

Chapter Eight: Linear Regression 127

Context and Perspective 127
Learning Objectives 127
Organizational Understanding 128
Data Understanding 128
Data Preparation 129
Modeling 131
Evaluation 132
Deployment 134
Chapter Summary 137
Review Questions 137
Exercise 138

Chapter Nine: Logistic Regression 141

Context and Perspective 141
Learning Objectives 141
Organizational Understanding 142
Data Understanding 142
Data Preparation 143
Modeling 147
Evaluation 148
Deployment 151
Chapter Summary 153
Review Questions 154
Exercise 154

Chapter Ten: Decision Trees 157

Context and Perspective 157
Learning Objectives 157
Organizational Understanding 158
Data Understanding 159
Data Preparation 161
Modeling 166
Evaluation 169
Deployment 171
Chapter Summary 172
Review Questions 172
Exercise 173

Chapter Eleven: Neural Networks 175

Context and Perspective 175
Learning Objectives 175
Organizational Understanding 175
Data Understanding 176
Data Preparation 178
Modeling 181
Evaluation 181
Deployment 184
Chapter Summary 186
Review Questions 187
Exercise 187

Chapter Twelve: Text Mining 189

Context and Perspective 189
Learning Objectives 189
Organizational Understanding 190
Data Understanding 190
Data Preparation 191
Modeling 202
Evaluation 203
Deployment 213
Chapter Summary 213
Review Questions 214
Exercise 214

SECTION THREE: Special Considerations in Data Mining

217

Chapter Thirteen: Evaluation and Deployment 219

How Far We’ve Come 219
Learning Objectives 220
Cross-Validation 221
Chapter Summary: The Value of Experience 227
Review Questions 228
Exercise 228

Chapter Fourteen: Data Mining Ethics 231

Why Data Mining Ethics? 231
Ethical Frameworks and Suggestions 233
Conclusion 235
GLOSSARY and INDEX 237
About the Author 251

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