A maximizing model of bezdek-like spherical fuzzy c-means

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this study, a maximizing model of Bezdek-like spherical fuzzy c-means clustering is proposed, which is based on the regularization of the maximizing model of spherical hard c-means. Such a maximizing model was unclear in Bezdek-like method, whereas other types of methods have been investigated well both in minimizing and maximizing model. Using theoretical analysis and numerical experiments, the classifi-cation rule of the proposed method is shown. Using numerical examples, the proposed method is shown to be valid for document clustering, because documents are represented as spherical data via term documentinverse document frequency weighting and normalization processing.

Original languageEnglish
Pages (from-to)662-669
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume19
Issue number5
Publication statusPublished - 2015 Sep 1

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Keywords

  • Fuzzy c-means
  • Spherical clustering

ASJC Scopus subject areas

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

Cite this

A maximizing model of bezdek-like spherical fuzzy c-means. / Kanzawa, Yuchi.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 19, No. 5, 01.09.2015, p. 662-669.

Research output: Contribution to journalArticle

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