q-Divergence Regularization of Bezdek-Type Fuzzy Clustering for Categorical Multivariate Data

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

Abstract

In this paper, the q-divergence-regularized Bezdek-type fuzzy clustering approach is proposed for categorical multivariate data. Because the approach proposed here reduces to the conventional methods via appropriate control of the fuzzification parameters, it is considered as a generalization. Further, numerical experiments were conducted to show that the proposed method outperformed the conventional method in terms of clustering accuracy.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 18th International Conference, MDAI 2021, Proceedings
EditorsVicenç Torra, Yasuo Narukawa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages218-230
Number of pages13
ISBN (Print)9783030855284
DOIs
Publication statusPublished - 2021
Event18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021 - Virtual, Online
Duration: 2021 Sep 272021 Sep 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12898 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021
CityVirtual, Online
Period21/9/2721/9/30

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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