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Cover image for Data Mining : Practical Machine Learning Tools and Techniques
Title:
Data Mining : Practical Machine Learning Tools and Techniques
Personal Author:
Series:
Morgan Kaufmann series in data management systems
Edition:
3rd ed.
Publication Information:
Burlington, MA : Morgan Kaufmann, 2011
Physical Description:
xxxiii, 629 p. : ill. ; 24 cm.
ISBN:
9780123748560
Subject Term:

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30000010236738 QA76.9.D34 W58 2011 Open Access Book Book
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Summary

Summary

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition , 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.


Author Notes

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 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.


Table of Contents

Part I Machine Learning Tools and Techniques
Ch 1 What's It All About?
Ch 2 Input: Concepts, Instances, Attributes
Ch 3 Output: Knowledge Representation: Algorithms
Ch 4 The Basic Methods
Ch 5 Credibility: Evaluating What's Been Learned
Ch 6 Implementations: Real Machine Learning Schemes
Ch 7 Data Transformation
Ch 8 Ensemble Learning
Ch 9 Massive Data Sets
Ch 10 Practical Data Mining
Part II The Weka Machine Learning Workbench
Ch 11 Intro to Weka
Ch 12 The Explorer
Ch 13 The Knowledge Flow Interface
Ch 14 The Experimenter
Ch 15 The Command-Line Interface
Ch 16 Embedded Machine Learning
Ch 17 Writing New Learning Schemes
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