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

By: Contributor(s): Material type: TextPublisher: Amsterdam, Netherlands : Elsevier/Morgan Kaufmann, [2011]Copyright date: ©2011Edition: Third editionDescription: xxxiii, 629 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780123748560
  • 0123748569
Subject(s): DDC classification:
  • 006.3/12 22
LOC classification:
  • QA76.9 WIT
Contents:
Part I. Machine Learning Tools and Techniques: 1. What's iIt 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.
Item type: Books
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books GSU Library Epoch General Stacks NFIC QA76.9WIT (Browse shelf(Opens below)) 1 Available 50000005011
Books GSU Library Epoch General Stacks NFIC QA76.9WIT (Browse shelf(Opens below)) 2 Available 50000005012

Includes bibliographical references (pages 587-605) and index.

Part I. Machine Learning Tools and Techniques: 1. What's iIt 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.

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