Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations / Edition 1

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations / Edition 1

ISBN-10:
1558605525
ISBN-13:
9781558605527
Pub. Date:
10/11/1999
Publisher:
Elsevier Science

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Overview

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations / Edition 1

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.

Product Details

ISBN-13: 9781558605527
Publisher: Elsevier Science
Publication date: 10/11/1999
Series: Morgan Kaufmann Series in Data Management Systems Series
Pages: 371
Product dimensions: 7.29(w) x 9.12(h) x 0.82(d)

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 Output
8. Nuts And Bolts: Machine Learning Algorithms In Java
9. Looking Forward

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