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A Latent Variable Pairwise Classification Model of a Clustering Ensemble

  • Vladimir Berikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)

Abstract

This paper addresses some theoretical properties of clustering ensembles. We consider the problem of cluster analysis from pattern recognition point of view. A latent variable pairwise classification model is proposed for studying the efficiency (in terms of ”error probability”) of the ensemble. The notions of stability, homogeneity and correlation between ensemble elements are introduced. An upper bound for misclassification probability is obtained. Numerical experiment confirms potential usefulness of the suggested ensemble characteristics.

Keywords

clustering ensemble latent variable model misclassification probability error bound ensemble’s homogeneity and correlation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Berikov
    • 1
  1. 1.Sobolev Institute of mathematicsNovosibirsk State UniversityRussia

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