A Generalization of Fuzzy c-Means with Variables Controlling Cluster Size

研究成果: Conference contribution

抄録

This study constructs two general fuzzy clustering algorithms with a cluster size controller. The first algorithm includes the standard fuzzy c-means (SFCM), modified SFCM, and generalized fuzzy c-means, and the second one includes the entropy-regularized fuzzy c-means (EFCM), modified EFCM (mEFCM), and regularized fuzzy c-means (RFCM). Furthermore, the results of this study demonstrate that the behavior of the fuzzy classification functions of the first proposed algorithm at points far from clusters are similar to that for mSFCM, and those of the second one are similar to those for EFCM, mEFCM, and RFCM. some conventional clustering algorithms.

本文言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 20th International Conference, MDAI 2023, Proceedings
編集者Vicenç Torra, Yasuo Narukawa
出版社Springer Science and Business Media Deutschland GmbH
ページ226-237
ページ数12
ISBN(印刷版)9783031334979
DOI
出版ステータスPublished - 2023
イベント20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023 - Umeå, Sweden
継続期間: 2023 6月 192023 6月 22

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)
13890 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023
国/地域Sweden
CityUmeå
Period23/6/1923/6/22

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一般

フィンガープリント

「A Generalization of Fuzzy c-Means with Variables Controlling Cluster Size」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル