Data mining: practical machine learning tools and techniques with Java implementations

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Morgan Kaufmann, 2000 - Computers - 371 pages
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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.

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Review: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)

User Review  - Thomasreece - Goodreads

C Cool book ,it makes you aware of mathematical inventions not too far fetched from wanting to incorporate only to know after that there was already such an invention. haven't completed Read full review

Review: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)

User Review  - Kenny Daily - Goodreads

Great reference book, with a good introduction to using the Weka suite. Read full review


Whats it all about?
1 The contact lens data
3 Weather data with some numeric attributes

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About the author (2000)

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 is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.