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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 18th International Conference, MDAI 2021, Proceedings
編集者Vicenç Torra, Yasuo Narukawa
出版社Springer Science and Business Media Deutschland GmbH
ページ218-230
ページ数13
ISBN(印刷版)9783030855284
DOI
出版ステータスPublished - 2021
イベント18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021 - Virtual, Online
継続期間: 2021 9 272021 9 30

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)
12898 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)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

  • 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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