Preface PART I INTRODUCTION TO DATA MINING CHAPTER 1What's it all about? 1.1 Data Mining and Machine Learning Describing Structural Patterns Machine Learning Data Mining 1.2 Simple Examples: The Weather Problem and Others The Weather Problem Contact Lenses: An Idealized Problem Irises: A Classic Numeric Dataset CPU Performance: Introducing Numeric Prediction Labor Negotiations: A More Realistic Example Soybean Classification: A Classic Machine Learning Success 1.3 Fielded Applications Web Mining Decisions Involving Judgment Screening Images Load Forecasting Diagnosis Marketing and Sales Other Applications 1.4 The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search Enumerating the Concept Space Bias 1.7 Data Mining and Ethics Reidentification Using Personal Information Wider Issues 1.8 Further Reading and Bibliographic Notes CHAPTER 2 Input: concepts, instances, attributes 2.1 What's a Concept? 2.2 What's in an Example? Relations Other Example Types 2.3 What's in an Attribute? 2.4 Preparing the Input Gathering the Data Together ARFF Format Sparse Data Attribute Types Missing Values Inaccurate Values Unbalanced Data Getting to Know Your Data 2.5 Further Reading and Bibliographic Notes CHAPTER 3 Output: knowledge representation 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules Classification Rules Association Rules Rules With Exceptions More Expressive Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading and Bibliographic Notes CHAPTER 4 Algorithms: the basic methods 4.1 Inferring Rudimentary Rules Missing Values and Numeric Attributes 4.2 Simple Probabilistic Modeling Missing Values and Numeric Attributes Naive Bayes for Document Classification Remarks 4.3 Divide-and-Conquer: Constructing Decision Trees Calculating Information Highly Branching Attributes …… CHAPTER 5 Credibility: evaluating what's been learned PART II MORE ADVANCED MACHINE LEARNING SCHEMES CHAPTER 6 Trees and rules CHAPTER 7 Extending instance-based and linear models CHAPTER 8 Data transformations CHAPTER 9 Probalicistic methods CHAPTER 10 Deep learning CHAPTER 11 Beyond supervised and unsupervised learning CHAPTER 12 Ensemble learning CHAPTER 13 Moving on: applications and beyond Appendix A: Theoretical foundations Appendix B: The WEKA workbench References Index
