PREFACE
Updated and Revised Content
Second Edition
Third Edition
ACKNOWLEDGMENTS
ABOUT THE AUTHORS
PART Ⅰ INTRODUCTION TO DATA MINING
CHAPTER 1 What's It All About?
CHAPTER 2 Input:Concepts,Instances,and Attributes
CHAPTER 3 Output:Knowledge Representation
CHAPTER 4 Algorithms:The Basic Methods
CHAPTER 5 Credibility:Evaluating What's Been Learned
PART Ⅱ ADVANCED DATA MINING
CHAPTER 6 Implementations:Real Machine Learning Schemes
CHAPTER 7 Data Transformations
CHAPTER 8 Ensemble Learning
CHAPTER 9 Moving on:Applications and Beyond
PART Ⅲ THE WEKA DATA MINING WORKBENCH
CHAPTER 10 Introduction to Weka
CHAPTER 11 The Explorer
CHAPTER 12 The Knowledge Flow Interface
CHAPTER 13 The Experimenter
CHAPTER 14 The Command-Line Interface
CHAPTER 15 Embedded Machine Learning
CHAPTER 16 Writing New Learning Schemes
CHAPTER 17 Tutorial Exercises for the Weka Explorer
REFERENCES
INDEX