Data Mining For Business Analytics – Concepts, Techniques, And Applications In R

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.

This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

• Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
• Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
• More than a dozen case studies demonstrating applications for the data mining techniques described
• End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions  www.dataminingbook.com

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Foreword by Gareth James xix

Foreword by Ravi Bapna xxi

Preface to the R Edition xxiii

Acknowledgments xxvii

PART I PRELIMINARIES

CHAPTER 1 Introduction 3

1.1 What Is Business Analytics? 3

1.2 What Is Data Mining? 5

1.3 Data Mining and Related Terms 5

1.4 Big Data 6

1.5 Data Science 7

1.6 Why Are There So Many Different Methods? 8

1.7 Terminology and Notation 9

1.8 Road Maps to This Book 11

Order of Topics 11

CHAPTER 2 Overview of the Data Mining Process 15

2.1 Introduction 15

2.2 Core Ideas in Data Mining 16

2.3 The Steps in Data Mining 19

2.4 Preliminary Steps 21

2.5 Predictive Power and Overfitting 33

2.6 Building a Predictive Model 38

2.7 Using R for Data Mining on a Local Machine 43

2.8 Automating Data Mining Solutions 43

PART II DATA EXPLORATION AND DIMENSION REDUCTION

CHAPTER 3 Data Visualization 55

3.1 Uses of Data Visualization 55

3.2 Data Examples 57

3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 59

3.4 Multidimensional Visualization 67

3.5 Specialized Visualizations 80

3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 86

CHAPTER 4 Dimension Reduction 91

4.1 Introduction 91

4.2 Curse of Dimensionality 92

4.3 Practical Considerations 92

4.4 Data Summaries 94

4.5 Correlation Analysis 97

4.6 Reducing the Number of Categories in Categorical Variables 99

4.7 Converting a Categorical Variable to a Numerical Variable 99

4.8 Principal Components Analysis 101

4.9 Dimension Reduction Using Regression Models 111

4.10 Dimension Reduction Using Classification and Regression Trees 111

PART III PERFORMANCE EVALUATION

CHAPTER 5 Evaluating Predictive Performance 117

5.1 Introduction 117