Data mining : practical machine learning tools and techniques /

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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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Détails bibliographiques
Cote:Libro Electrónico
Auteur principal: Witten, I. H. (Ian H.)
Autres auteurs: Frank, Eibe
Format: Électronique eBook
Langue:Inglés
Publié: Amsterdam ; Boston, MA : Morgan Kaufman, 2005.
Édition:Second edition.
Collection:Morgan Kaufmann series in data management systems.
Sujets:
Data mining.
Data Mining
Exploration de données (Informatique)
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Data mining
Java (programmeertaal)
Machine-learning.
Algoritmen.
Descoberta de conhecimento.
Mineração de dados.
Accès en ligne:Texto completo
Table des matières:
  • 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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