Generalized fuzzy c-means clustering and its property of fuzzy classification function

Yuchi Kanzawa, Sadaaki Miyamoto

Research output: Contribution to journalArticlepeer-review

Abstract

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits a behavior similar to that of both standard and exponential fuzzy c-means clustering.

Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume25
Issue number1
DOIs
Publication statusPublished - 2021 Jan 20

Keywords

  • Fuzzy c-means clustering
  • Fuzzy classification function

ASJC Scopus subject areas

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

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