Data Mining: Practical Machine Learning Tools and Techniques, Second Edition / Edition 2

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition / Edition 2

by Ian H. Witten, Eibe Frank
     
 

ISBN-10: 0120884070

ISBN-13: 9780120884070

Pub. Date: 06/08/2005

Publisher: Elsevier Science

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical

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Overview

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these 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 for the 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; plus much more.

• Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods
• Performance improvement techniques that work by transforming the input or output
• Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface

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Product Details

ISBN-13:
9780120884070
Publisher:
Elsevier Science
Publication date:
06/08/2005
Series:
Morgan Kaufmann Series in Data Management Systems Series
Edition description:
REV
Pages:
560
Product dimensions:
1.13(w) x 9.25(h) x 7.50(d)

Related Subjects

Table of Contents

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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