Data Mining, English edition

PART I: Introduction to Data Mining Ch 1 What's It All About? Ch 2 Input: Concepts, Instances, Attributes Ch 3 Output: Knowledge Representation Ch 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data Mining

Ch 6 Implementations: Real Machine Learning Schemes Ch 7 Data Transformation Ch 8 Ensemble Learning Ch 9 Moving On: Applications and Beyond PART III: The Weka Data MiningWorkbench Ch 10 Introduction to Weka Ch 11 The Explorer Ch 12 The Knowledge Flow Interface Ch 13 The Experimenter Ch 14 The Command-Line Interface Ch 15 Embedded Machine Learning Ch 16 Writing New Learning Schemes Ch 17 Tutorial Exercises for the Weka Explorer

Mark A. Hall 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.

"Co-author Witten is the author of other well-known books on data mining, and he and his co-authors of this book excel in statistics, computer science, and mathematics. Their in- depth backgrounds and insights are the strengths that have permitted them to avoid heavy mathematical derivations in explaining machine learning algorithms so they can help readers from different fields understand algorithms. I strongly recommend this book to all newcomers to data mining, especially to those who wish to understand the fundamentals of machine learning algorithms."--INFORMS Journal of Computing "The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project."--Book News, Reference & Research "I would recommend this book to anyone who is getting started in either data mining or machine learning and wants to learn how the fundamental algorithms work. I liked that the book slowly teaches you the different algorithms piece by piece and that there are also a lot of examples. I plan on taking a machine learning course this upcoming fall semester and feel that the book gave me great insight that the course will be based on mathematics more than I had originally expected. My favorite part of the book was the last chapter where it

"...offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations."

"Co-author Witten is the author of other well-known books on data mining, and he and his co-authors of this book excel in statistics, computer science, and mathematics. Their in- depth backgrounds and insights are the strengths that have permitted them to avoid heavy mathematical derivations in explaining machine learning algorithms so they can help readers from different fields understand algorithms. I strongly recommend this book to all newcomers to data mining, especially to those who wish to understand the fundamentals of machine learning algorithms."-- INFORMS Journal of Computing

"The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project." -- Book News, Reference & Research

"I would recommend this book to anyone who is getting started in either data mining or machine learning and wants to learn how the fundamental algorithms work. I liked that the book slowly teaches you the different algorithms piece by piece and that there are also a lot of examples. I plan on taking a machine learning course this upcoming fall