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Data Mining for the Masses

Dr. Matthew North

A Global Text Project Book

This book is available on Amazon.com.

This book is licensed under a Creative Commons Attribution 3.0 License

ISBN: 0615684378

ISBN-13: 978-0615684376

DEDICATION

This book is gratefully dedicated to Dr. Charles Hannon, who gave me the chance to become a college professor and then challenged me to learn how to teach data mining to the masses.

iii

Data Mining for the Masses
Table of Contents
Dedication .......................................................................................................................................................	iii
Table of Contents............................................................................................................................................	v
Acknowledgements ........................................................................................................................................	xi
SECTION ONE: Data Mining Basics.........................................................................................................	1
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
v

Data Mining for the Masses
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
vi

Data Mining for the Masses
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
vii

	Data Mining for the Masses
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
	viii

	Data Mining for the Masses
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
	ix

Data Mining for the Masses

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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