Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
Language: English
Published by Morgan Kaufmann, 1999
- Softcover
- Used
Seller: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc
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Condition: Used - Good
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Add to basketItem description from seller
Discusses machine learning concepts. This work offers practical advice on applying machine learning tools and techniques in real-world data mining situations. It helps you learn about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods.
Seller Inventory # CIN1558605525G
- Title
- Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
- Author
- Ian H Witten
- Publisher
- Morgan Kaufmann
- Publication year
- 1999
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1558605525
- ISBN 13
- 9781558605527
This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining―including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.
Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.
* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.* Covers performance improvement techniques, including input preprocessing and combining output from different methods.* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs."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.
"About the title" may belong to another edition of this title.
World of Books Inc
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AbeBooks seller since March 23, 2026
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