On Simultaneous Selection of Prototypes and Features in Large Data

  • T. Ravindra Babu
  • M. Narasimha Murty
  • V. K. Agrawal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

In dealing with high-dimensional, large data, for the sake of abstract generation one resorts to either dimensionality reduction or cluster the patterns and deal with cluster representatives or both. The current paper examines whether there exists an equivalence in terms of generalization error. Four different approaches are followed and results of exercises are provided in driving home the issues involved.

Keywords

Data Mining prototype selection frequent itemsets clustering feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • T. Ravindra Babu
    • 1
  • M. Narasimha Murty
    • 1
  • V. K. Agrawal
    • 2
  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia
  2. 2.ISRO Satellite CentreBangaloreIndia

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