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Sequential Sparse Adaptive Possibilistic Clustering

  • Spyridoula D. Xenaki
  • Konstantinos D. Koutroumbas
  • Athanasios A. Rontogiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8445)

Abstract

Possibilistic clustering algorithms have attracted considerable attention, during the last two decades. A major issue affecting the performance of these algorithms is that they involve certain parameters that need to be estimated accurately beforehand and remain fixed during their execution. Recently, a possibilistic clustering scheme has been proposed that allows the adaptation of these parameters and imposes sparsity in the sense that it forces the data points to “belong” to only a few (or even none) clusters. The algorithm does not require prior knowledge of the exact number of clusters but, rather, only a crude overestimate of it. However, it requires the estimation of two additional parameters. In this paper, a sequential version of this scheme is proposed, which possesses all the advantages of its ancestor and in addition, it requires the (crude) estimation of just a single parameter. Simulation results are provided that show the effectiveness of the proposed algorithm.

Keywords

possibilistic clustering parameter adaptivity sparsity sequential processing k-means fuzzy c-means 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Spyridoula D. Xenaki
    • 1
  • Konstantinos D. Koutroumbas
    • 1
  • Athanasios A. Rontogiannis
    • 1
  1. 1.IAASARSNational Observatory of AthensPenteliGreece

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