Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

Yuchi Kanzawa, Sadaaki Miyamoto

研究成果: Conference contribution

抜粋

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers standard fuzzy c-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits similar behavior to that of standard fuzzy c-means clustering.

元の言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings
編集者Vicenc Torra, Vicenc Torra, Yasuo Narukawa, Manuel González-Hidalgo, Isabel Aguilo
出版者Springer Verlag
ページ243-254
ページ数12
ISBN(印刷物)9783030002015
DOI
出版物ステータスPublished - 2018 1 1
イベント15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 - Palma de Mallorce, Spain
継続期間: 2018 10 152018 10 18

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)
11144 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018
Spain
Palma de Mallorce
期間18/10/1518/10/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Kanzawa, Y., & Miyamoto, S. (2018). Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. : V. Torra, V. Torra, Y. Narukawa, M. González-Hidalgo, & I. Aguilo (版), Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings (pp. 243-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 11144 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-00202-2_20