Data mining : practical machine learning tools and techniques
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Burlington, MA
Name of Publisher, Distributor, etc.
Morgan Kaufmann
Date of Publication, Distribution, etc.
2011
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xxxiii, 629 p. : ill. ; 24 cm
SERIES
Series Title
]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
NOTES PERTAINING TO EDITION AND BIBLIOGRAPHIC HISTORY
Text of Note
3rd ed
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
TOPICAL NAME USED AS SUBJECT
Entry Element
، Data mining
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA
76
.
9
.
D343
W58
2011
PERSONAL NAME - PRIMARY RESPONSIBILITY
Relator Code
AU
AU Frank, Eibe
AU Hall, Mark A
TI
SE
SE Morgan Kaufmann series in data management systems