Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.

Format:

Book

Author/Creator:

Witten, I. H. (Ian H.)

Contributor:

Frank, Eibe.

Hall, Mark A.

Series:

The Morgan Kaufmann Series in Data Management Systems

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (665 p.) : ill.

Edition:

3rd ed.

Place of Publication:

Amsterdam : Elsevier/Morgan Kaufmann, 2011.

Language Note:

English

System Details:

text file

Summary:

Data Mining: Practical Machine Learning Tools and Techniques 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizatio

Contents:

Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned

Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond

Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer

12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.

Notes:

Description based upon print version of record

Includes bibliographical references and index.

ISBN:

9786612953880

9781282953888

1282953885

9780080890364

0080890369

OCLC:

701704090