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Data Mining: Practical Machine Learning Tools and Techniques 4/e

作者：Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
原價：NT$ 2,100

ISBN：9780128042915
版次：4
年份：2017
出版商：Elsevier B.V.
頁數/規格：654頁/平裝單色
參考網頁：Data Mining 4/e

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Description
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book

Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book

Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

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Key Features

Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects

Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods

Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface

Includes open-access online courses that introduce practical applications of the material in the book

Table of Contents
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: More advanced machine learning schemes
Chapter 6. Trees and rules
Chapter 7. Extending instance-based and linear models
Chapter 8. Data transformations
Chapter 9. Probabilistic methods
Chapter 10. Deep learning
Chapter 11. Beyond supervised and unsupervised learning
Chapter 12. Ensemble learning
Chapter 13. Moving on: applications and beyond Abstract

Appendix A. Theoretical foundations
Appendix B. The WEKA workbench
References
Index

Ian Witten
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.

Affiliations and Expertise
Professor, Computer Science Department, University of Waikato, New Zealand.

Eibe Frank
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.>

Affiliations and Expertise
Associate Professor, Department of Computer Science, University of Waikato, Hamilton, New Zealand

Mark Hall
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.

Affiliations and Expertise
Honorary Research Associate, Computer Science Department, University of Waikato, New Zealand

Christopher Pal
Affiliations and Expertise
Associate Professor, Département de génie informatique et génie logiciel, Polytechnique Montréal, and the Montréal Institute for Learning Algorithms at the Université de Montréal, Canada.

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