Data Mining for Business Intelligence_ Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner [Shmueli, Patel & Bruce 2010-10-26].pdf

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Contents
Foreword
Preface to the second edition
Preface to the first edition
Acknowledgments
PART I 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
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
PROBLEMS
PART II 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
PROBLEMS
Chapter 4 Dimension Reduction
3
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
PROBLEMS
PART III PERFORMANCE EVALUATION
Chapter 5 Evaluating Classification and Predictive
Performance
5.1 Introduction
5.2 Judging Classification Performance
5.3 Evaluating Predictive Performance
4
PROBLEMS
PART IV 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
PROBLEMS
Chapter 7 k-Nearest Neighbors (k-NN)
7.1 k-NN Classifier (categorical outcome)
7.2 k-NN for a Numerical Response
7.3 Advantages and Shortcomings of k-NN Algorithms
PROBLEMS
Chapter 8 Naive Bayes
8.1 Introduction
8.2 Applying the Full (Exact) Bayesian Classifier
5

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