Data mining : practical machine learning tools and techniques
著者
書誌事項
Data mining : practical machine learning tools and techniques
（The Morgan Kaufmann series in data management systems）
Elsevier , Morgan Kaufmann Publishers, c2005
2nd ed
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注記
"Morgan Kaufmann Publishers is an imprint of Elsevier"--T.p. verso
Includes bibliographical references (p. 485-503) and index
内容説明・目次
内容説明
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
目次
Preface
1. What's it all about?
2. Input: Concepts, instances, attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
5. Credibility: Evaluating what's been learned
6. Implementations: Real machine learning schemes
7. Transformations: Engineering the input and output
8. Moving on: Extensions and applications
Part II: The Weka machine learning workbench
9. Introduction to Weka
10. The Explorer
11. The Knowledge Flow interface
12. The Experimenter
13. The command-line interface
14. Embedded machine learning
15. Writing new learning schemes
References
Index
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