Data mining : practical machine learning tools and techniques with Java implementations
Author(s)
Bibliographic Information
Data mining : practical machine learning tools and techniques with Java implementations
（The Morgan Kaufmann series in data management systems）
Morgan Kaufmann, c2000
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Note
前付頁xxvi p.の刷もあり
Includes bibliographical references (p. 339-349) and index
Description and Table of Contents
Description
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.
Table of Contents
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. Moving On: Engineering The Input And Output8. Nuts And Bolts: Machine Learning Algorithms In Java 9. Looking Forward
by "Nielsen BookData"
