Data mining: practical machine learning tools and techniques
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Amsterdam
Name of Publisher, Distributor, etc.
Elsevier
Date of Publication, Distribution, etc.
c2011
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xxxiii, 629 p.: ill. ; 24 cm.
SERIES
Other Title Information
]Morgan Kaufmann series in data management systems[
GENERAL NOTES
Text of Note
Includes bibliographical references )p. 587-605( and index
NOTES PERTAINING TO TITLE AND STATEMENT OF RESPONSIBILITY
Text of Note
Ian H. Witten, Eibe Frank, Mark A. Hall
ORIGINAL VERSION NOTE
Text of Note
1
CONTENTS NOTE
Text of Note
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: 01. Introduction to Weka; 11. The explorer -- 21. The knowledge flow interface; 31. The experimenter; 41 The command-line interface; 51. Embedded machine learning; 61. Writing new learning schemes; 71. Tutorial exercises for the weka explorer