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Constraint Selection for Semi-supervised Topological Clustering

  • Kais Allab
  • Khalid Benabdeslem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

In this paper, we propose to adapt the batch version of self-organizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones.

Keywords

Constrain selection semi-supervised clustering SOM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kais Allab
    • 1
  • Khalid Benabdeslem
    • 1
  1. 1.GAMA LaboratoryUniversity of Lyon1VilleurbanneFrance

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