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

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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 al...

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Bibliographic Details
Online Access: Full Text (via EBSCO)
Main Author: Witten, I. H. (Ian H.)
Other Authors: Frank, Eibe
Format: eBook
Language:English
Published: Amsterdam ; Boston, MA : Morgan Kaufman, 2005.
Edition:Second edition.
Series:Morgan Kaufmann series in data management systems.
Subjects:
Data mining.
Table of Contents:
  • pt. I. Machine learning tools and techniques. What's it all about?
  • Input : concepts, instances, and attributes
  • Output : knowledge representation
  • Algorithms : the basic methods
  • Credibility : evaluating what's been learned
  • Implementations : real machine learning schemes
  • Transformations : engineering the input and output
  • Moving on : extensions and applications
  • pt. II. The Weka machine learning workbench. Introduction to Weka
  • The Explorer
  • The Knowledge Flow interface
  • The Experimenter
  • The command-line interface
  • Embedded machine learning
  • Writing new learning schemes.

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