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Clustering Quality Evaluation Based on Fuzzy FCA

  • Minyar Sassi
  • Amel Grissa Touzi
  • Habib Ounelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)

Abstract

Because clustering is an unsupervised procedure, clustering results need be judged by external criteria called validity indices. These indices play an important role in determining the number of clusters in a given dataset. A general approach for determining this number is to select the optimal value of a certain cluster validity index. Most existing indices give good results for data sets with well separated clusters, but usually fail for complex data sets, for example, data sets with overlapping clusters. In this paper, we propose a new approach for clustering quality evaluation while combining fuzzy logic with Formal Concept Analysis based on concept lattice. We define a formal quality index including the separation degree and the overlapping rate.

Keywords

Clustering Quality Overlapping Rate Separation Degree Validity Index Formal Concept Analyis Fuzzy Concept Lattice 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Minyar Sassi
    • 1
  • Amel Grissa Touzi
    • 1
  • Habib Ounelli
    • 2
  1. 1.Ecole Nationale d’Ingénieurs de Tunis, Bp. 37, Le Belvédère 1002 TunisTunisia
  2. 2.Faculté des Sciences de Tunis, Campus Universitaire -1060 TunisTunisia

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