Power-regularized fuzzy c-means clustering with a fuzzification parameter less than one

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2 Citations (Scopus)


The present study proposes two types of powerregularized fuzzy c-means (pFCM) clustering algorithms with a fuzzification parameter less than one, which supplements previous work on pFCM with a fuzzification parameter greater than one. Both the proposed methods are essentially identical to each other, but not when fuzzification parameter values are specified. Theoretical discussion reveals the property of the proposed methods, and some numerical results substantiate the property of the proposedmethods and show that the proposed methods outperform two conventional methods from an accuracy point of view.

Original languageEnglish
Pages (from-to)561-570
Number of pages10
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number4
Publication statusPublished - 2016


  • Fuzzy clustering
  • Power regularization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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