Data mining : practical machine learning tools and techniques

Data mining : practical machine learning tools and techniques /

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 o...

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Bibliographic Details
Online Access: Full text (MCPHS users only)
Main Authors: Witten, I. H. (Ian H.) (Author), Frank, Eibe (Author), Hall, Mark A. (Mark Andrew) (Author)
Format: Electronic eBook
Language:English
Published: Burlington, MA : Morgan Kaufmann, 2011
Edition:3rd ed.
Series:Morgan Kaufmann series in data management systems.
Subjects:
Data mining.
Data Mining
dissertations.
Academic theses.
Local Note:ProQuest Ebook Central
Table of Contents:
  • 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
  • Data transformation
  • Ensemble learning
  • Moving on : applications and beyond
  • Introduction to Weka
  • The explorer
  • The knowledge flow interface
  • The experimenter
  • The command-line interface
  • Embedded machine learning
  • Writing new learning schemes
  • Tutorial exercises for the weka explorer.

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