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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings
編集者Vicenc Torra, Yasuo Narukawa, Jordi Nin, Núria Agell
出版社Springer
ページ119-131
ページ数13
ISBN(印刷版)9783030575236
DOI
出版ステータスPublished - 2020
イベント17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 - Sant Cugat del Vallès, Spain
継続期間: 2020 9 22020 9 4

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12256 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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