Contents
Foreword
Preface to the Second Edition
Preface to the First Edition
Acknowledgments
PART ONE: Preliminaries
CHAPTER 1: Introduction
1.1 WHAT IS DATA MINING?
1.2 WHERE IS DATA MINING USED?
1.3 ORIGINS OF DATA MINING
1.4 RAPID GROWTH OF DATA MINING
1.5 WHY ARE THERE SO MANY DIFFERENT METHODS?
1.6 TERMINOLOGY AND NOTATION
1.7 ROAD MAPS TO THIS BOOK
CHAPTER 2: Overview of the Data Mining Process
2.1 INTRODUCTION
2.2 CORE IDEAS IN DATA MINING
2.3 SUPERVISED AND UNSUPERVISED LEARNING
2.4 STEPS IN DATA MINING
2.5 PRELIMINARY STEPS
2.6 BUILDING A MODEL: EXAMPLE WITH LINEAR REGRESSION
2.7 USING EXCEL FOR DATA MINING
PART TWO: Data Exploration and Dimension Reduction
CHAPTER 3: Data Visualization
3.1 USES OF DATA VISUALIZATION
3.2 DATA EXAMPLES
3.3 BASIC CHARTS: BAR CHARTS, LINE GRAPHS, AND SCATTERPLOTS
3.4 MULTIDIMENSIONAL VISUALIZATION
3.5 SPECIALIZED VISUALIZATIONS
3.6 SUMMARY OF MAJOR VISUALIZATIONS AND OPERATIONS,
ACCORDING TO DATA MINING GOAL
CHAPTER 4: Dimension Reduction
4.1 INTRODUCTION
4.2 PRACTICAL CONSIDERATIONS
4.3 DATA SUMMARIES
4.4 CORRELATION ANALYSIS
4.5 REDUCING THE NUMBER OF CATEGORIES IN CATEGORICAL
VARIABLES
4.6 CONVERTING A CATEGORICAL VARIABLE TO A NUMERICAL
VARIABLE
4.7 PRINCIPAL COMPONENTS ANALYSIS
4.8 DIMENSION REDUCTION USING REGRESSION MODELS
4.9 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION
TREES
PART THREE: Performance Evaluation
CHAPTER 5: Evaluating Classification and Predictive Performance
5.1 INTRODUCTION
5.2 JUDGING CLASSIFICATION PERFORMANCE
5.3 EVALUATING PREDICTIVE PERFORMANCE
PART FOUR: Prediction and Classification Methods
CHAPTER 6: Multiple Linear Regression
6.1 INTRODUCTION
6.2 EXPLANATORY VERSUS PREDICTIVE MODELING
6.3 ESTIMATING THE REGRESSION EQUATION AND PREDICTION
6.4 VARIABLE SELECTION IN LINEAR REGRESSION
CHAPTER 7: k-Nearest Neighbors; (k-NN)
7.1 k-NN CLASSIFIER (CATEGORICAL OUTCOME)
7.2 kNN FOR A NUMERICAL RESPONSE
7.3 ADVANTAGES AND SHORTCOMINGS OF k-NN ALGORITHMS
CHAPTER 8: Naive Bayes
8.1 INTRODUCTION
8.2 APPLYING THE FULL (EXACT) BAYESIAN CLASSIFIER
8.3 ADVANTAGES AND SHORTCOMINGS OF THE NAIVE BAYES
CLASSIFIER
CHAPTER 9: Classification and Regression Trees
9.1 INTRODUCTION
9.2 CLASSIFICATION TREES
9.3 MEASURES OF IMPURITY
9.4 EVALUATING THE PERFORMANCE OF A CLASSIFICATION TREE
9.5 AVOIDING OVERFITTING
9.6 CLASSIFICATION RULES FROM TREES
9.7 CLASSIFICATION TREES FOR MORE THAN TWO CLASSES
9.8 REGRESSION TREES
9.9 ADVANTAGES, WEAKNESSES, AND EXTENSIONS
CHAPTER 10: Logistic Regression
10.1 INTRODUCTION
10.2 LOGISTIC REGRESSION MODEL
10.3 EVALUATING CLASSIFICATION PERFORMANCE
10.4 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED
FLIGHTS
10.5 APPENDIX: LOGISTIC REGRESSION FOR PROFILING
CHAPTER 11: Neural Nets
11.1 INTRODUCTION
11.2 CONCEPT AND STRUCTURE OF A NEURAL NETWORK
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