Generalization property of fuzzy classification function for tsallis entropy-regularization of bezdek-type fuzzy c-means clustering

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

Abstract

In this study, Tsallis entropy-regularized Bezdek-type fuzzy c-means clustering method is proposed. Because the proposed method reduces to four conventional fuzzy clustering methods by appropriately controlling fuzzification parameters, the proposed method is considered to be their generalization. Through numerical experiments, this generalization property is confirmed; in addition, it is observed that the fuzzy classification function of the proposed method approaches a value equal to the reciprocal of the cluster number.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
EditorsVicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
PublisherSpringer
Pages119-131
Number of pages13
ISBN (Print)9783030575236
DOIs
Publication statusPublished - 2020
Event17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Spain
Duration: 2020 Sep 22020 Sep 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12256 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020
Country/TerritorySpain
CitySant Cugat del Vallès
Period20/9/220/9/4

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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