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Ian H. Witten

Christopher J. Pal

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

by Ian H. Witten (Author), Eibe Frank (Author), Mark A. Hall (Author), Format: Paperback

(137)

Part of: The Morgan Kaufmann Series in Data Management Systems (52 books)

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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.

Please visit the book companion website.

It contains

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.

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.

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ISBN-10

0128042915
ISBN-13

978-0128042915
Edition

4th
Publisher

Morgan Kaufmann
Publication date

December 1, 2016
Part of series

The Morgan Kaufmann Series in Data Management Systems
Language

English
Dimensions

7.5 x 1.48 x 9.25 inches
Print length

654 pages

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Editorial Reviews

Review

"...this volume is the most accessible introduction to data mining to appear in recent years. It is worthy of a fourth edition." --Computing Reviews

Review

This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning provides practical advice and techniques

From the Back Cover

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the 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 will teach you everything you 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. Witten, Frank, Hall and Pal include the techniques of today as well as methods at the leading edge of contemporary research.

Key Features Include:

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

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

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

Accompanying open-access online courses that introduce practical application of the material in the book.

About the Author

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.

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 a 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 several articles on machine learning and data mining and has refereed for conferences and journals in these areas.

Christopher J. Pal is a Canada CIFAR AI Chair and a full professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Pal’s research interests include computer vision and pattern recognition, computational photography, natural language processing, statistical machine learning and applications to human computer interaction.

Publisher ‏ : ‎ Morgan Kaufmann
Publication date ‏ : ‎ December 1, 2016
Edition ‏ : ‎ 4th
Language ‏ : ‎ English
Print length ‏ : ‎ 654 pages
ISBN-10 ‏ : ‎ 0128042915
ISBN-13 ‏ : ‎ 978-0128042915
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 7.5 x 1.48 x 9.25 inches
Part of series ‏ : ‎ The Morgan Kaufmann Series in Data Management Systems
Best Sellers Rank: #266,955 in Books (See Top 100 in Books)
- #19 in Library Management
- #39 in Management Information Systems
- #80 in Data Mining (Books)
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Customers find the book to be a great textbook for data mining, appreciating its practical emphasis. However, the readability receives mixed feedback, with some customers finding it easy to read while others say it's very difficult to follow.

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18 customers mention content, 13 positive, 5 negative

Customers find the book comprehensive and suitable as a textbook, particularly appreciating its practical emphasis. One customer notes that it includes theoretical literature pointers.

Good book to have in your shelfRead more

Very good book for my master's thesis. Easy to understand and read.Read more

...The book was required for class, and I have no idea why. It is complete junk and waste of time and money!...Read more

Great book on how to use data mining for warRead more

6 customers mention readability, 2 positive, 4 negative

Customers have mixed opinions about the book's readability, with some finding it easy to read while others describe it as very difficult to read through and hard to follow for regular readers.

...It can be very hard to follow.Read more

Very good book for my master's thesis. Easy to understand and read.Read more

...I did not benefit from reading this book at all. It is painful to try to read, very boring and not educational....Read more

Maximum zoom and the equations (which i need for class) are impossible to read which renders the book mostly useless.Read more

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