Cluster-Dependent Feature Selection through a Weighted Learning Paradigm

  • Nistor Grozavu
  • Younès Bennani
  • Mustapha Lebbah
Part of the Studies in Computational Intelligence book series (SCI, volume 292)

Abstract

This paper addresses the problem of selecting a subset of the most relevant features from a dataset through a weighted learning paradigm.We propose two automated feature selection algorithms for unlabeled data. In contrast to supervised learning, the problem of automated feature selection and feature weighting in the context of unsupervised learning is challenging, because label information is not available or not used to guide the feature selection. These algorithms involve both the introduction of unsupervised local feature weights, identifying certain relevant features of the data, and the suppression of the irrelevant features using unsupervised selection. The algorithms described in this paper provide topographic clustering, each cluster being associated to a prototype and a weight vector, reflecting the relevance of the feature. The proposed methods require simple computational techniques and are based on the self-organizing map (SOM) model. Empirical results based on both synthetic and real datasets from the UCI repository, are given and discussed.

Keywords

Topographic Clustering Self-organizing Map Unsupervised Features Selection Cluster Characterization Weighted Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nistor Grozavu
    • 1
  • Younès Bennani
    • 1
  • Mustapha Lebbah
    • 1
  1. 1.LIPN-UMR 7030Université Paris 13VilletaneuseFrance

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