Data Mining

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann. Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.> Mark A. Hall was born in England but moved to New Zealand with his parents as a young boy. He now lives with his wife and four young children in a small town situated within an hour's drive of the University of Waikato. He holds a bachelor's degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.

List Of Figures
List Of Tables
Preface
Acknowledgments
About the Author

PART I: Introduction to Data Mining

Chapter: 1 What's It All About?
Chapter: 2 Input: Concepts, Instances, 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 Transformation
Chapter: 8 Ensemble Learning
Chapter: 9 Moving On: Applications and Beyond

PART III: The Weka Data MiningWorkbench

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