A Self-tuning Possibilistic c-Means Clustering Algorithm

  • László SzilágyiEmail author
  • Szidónia Lefkovits
  • Zsolt Levente Kucsván
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)


Most c-means clustering models have serious difficulties when facing clusters of different sizes and severely outlier data. The possibilistic c-means (PCM) algorithm can handle both problems to some extent. However, its recommended initialization using a terminal partition produced by the probabilistic fuzzy c-means does not work when severe outliers are present. This paper proposes a possibilistic c-means clustering model that uses only three parameters independently of the number of clusters, which is able to more robustly handle the above mentioned obstacles. Numerical evaluation involving synthetic and standard test data sets prove the advantages of the proposed clustering model.


Fuzzy c-means clustering Possibilistic c-means clustering Cluster size sensitivity Outlier data 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • László Szilágyi
    • 1
    • 2
    Email author
  • Szidónia Lefkovits
    • 3
  • Zsolt Levente Kucsván
    • 1
  1. 1.Computational Intelligence Research GroupSapientia - Hungarian Science University of TransylvaniaTîrgu MureşRomania
  2. 2.Department of Control Engineering and Information TechnologyBudapest University of Technology and EconomicsBudapestHungary
  3. 3.Department of InformaticsPetru Maior UniversityTîrgu MureşRomania

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