A Clustering Approach Using Weighted Similarity Majority Margins

  • Raymond Bisdorff
  • Patrick Meyer
  • Alexandru-Liviu Olteanu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7120)

Abstract

We propose a meta-heuristic algorithm for clustering objects that are described on multiple incommensurable attributes defined on different scale types. We make use of a bipolar-valued dual similarity-dissimilarity relation and perform the clustering process by first finding a set of cluster cores and then building a final partition by adding the objects left out to a core in a way which best fits the initial bipolar-valued similarity relation.

Keywords

Maximal Clique Community Detection Discrimination Threshold Cluster Core Cluster Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Raymond Bisdorff
    • 1
  • Patrick Meyer
    • 2
    • 3
  • Alexandru-Liviu Olteanu
    • 1
    • 2
    • 3
  1. 1.CSC/ILIAS, FSTCUniversity of LuxembourgLuxembourg
  2. 2.Institut Télécom, Télécom Bretagne, UMR CNRS 3192Lab-STICC, Technopôle Brest IroiseBrest Cedex 3France
  3. 3.Université Européenne de BretagneFrance

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