Regularized fuzzy c-means clustering and its behavior at point of infinity

Yuchi Kanzawa, Sadaaki Miyamoto

Research output: Contribution to journalArticle

Abstract

This study shows that a general regularized fuzzy cmeans (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.

Original languageEnglish
Pages (from-to)485-492
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume23
Issue number3
DOIs
Publication statusPublished - 2019 May 1

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Clustering algorithms
Derivatives
Fuzzy clustering

Keywords

  • Fuzzy clustering
  • regularization

ASJC Scopus subject areas

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

Cite this

Regularized fuzzy c-means clustering and its behavior at point of infinity. / Kanzawa, Yuchi; Miyamoto, Sadaaki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 23, No. 3, 01.05.2019, p. 485-492.

Research output: Contribution to journalArticle

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