Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Witten, Ian H.; Frank, Eibe; Hall, Mark A.
Language: English
Published by Morgan Kaufmann, 2011
Series: Book 20 of 22 - The Morgan Kaufmann Series in Data Management Systems
- Softcover
- Used
Seller: Lexington Books Inc, Idaho Falls, ID, U.S.A.Lexington Books Inc
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- Title
- Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
- Author
- Witten, Ian H.; Frank, Eibe; Hall, Mark A.
- Publisher
- Morgan Kaufmann
- Publication year
- 2011
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0123748569
- ISBN 13
- 9780123748560
- Edition
- 3rd Edition
- Item weight
- 49 ounces
- Dimensions
- 9x7x1
- Series
- Book 20 of 22: The Morgan Kaufmann Series in Data Management Systems
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.
- Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
- Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
- Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks―in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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