Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

Yuchi Kanzawa, Sadaaki Miyamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers standard 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 similar behavior to that of standard fuzzy c-means clustering.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings
EditorsVicenc Torra, Vicenc Torra, Yasuo Narukawa, Manuel González-Hidalgo, Isabel Aguilo
PublisherSpringer Verlag
Pages243-254
Number of pages12
ISBN (Print)9783030002015
DOIs
Publication statusPublished - 2018 Jan 1
Event15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 - Palma de Mallorce, Spain
Duration: 2018 Oct 152018 Oct 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018
CountrySpain
CityPalma de Mallorce
Period18/10/1518/10/18

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Keywords

  • Fuzzy c-means clustering
  • Fuzzy classification function

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kanzawa, Y., & Miyamoto, S. (2018). Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. In V. Torra, V. Torra, Y. Narukawa, M. González-Hidalgo, & I. Aguilo (Eds.), Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings (pp. 243-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11144 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-00202-2_20