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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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
Volume20
Issue number4
DOIs
Publication statusPublished - 2016

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Clustering algorithms

Keywords

  • Fuzzy clustering
  • Power regularization

ASJC Scopus subject areas

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

Cite this

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abstract = "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.",
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