Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

Yuchi Kanzawa; Sadaaki Miyamoto

doi:10.1007/978-3-030-00202-2_20

Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

Yuchi Kanzawa, Sadaaki Miyamoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings
Editors	Vicenc Torra, Vicenc Torra, Yasuo Narukawa, Manuel González-Hidalgo, Isabel Aguilo
Publisher	Springer Verlag
Pages	243-254
Number of pages	12
ISBN (Print)	9783030002015
DOIs	https://doi.org/10.1007/978-3-030-00202-2_20
Publication status	Published - 2018 Jan 1
Event	15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 - Palma de Mallorce, Spain Duration: 2018 Oct 15 → 2018 Oct 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11144 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018
Country/Territory	Spain
City	Palma de Mallorce
Period	18/10/15 → 18/10/18

Keywords

Fuzzy c-means clustering
Fuzzy classification function

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-00202-2_20

Cite this

Kanzawa, Y., & Miyamoto, S. (2018). Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. In V. Torra, V. Torra, Y. Narukawa, M. González-Hidalgo, & I. Aguilo (Eds.), 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); Vol. 11144 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-00202-2_20

Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. / Kanzawa, Yuchi; Miyamoto, Sadaaki.
Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings. ed. / Vicenc Torra; Vicenc Torra; Yasuo Narukawa; Manuel González-Hidalgo; Isabel Aguilo. Springer Verlag, 2018. p. 243-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11144 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kanzawa, Y & Miyamoto, S 2018, Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. in V Torra, V Torra, Y Narukawa, M González-Hidalgo & I Aguilo (eds), Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11144 LNAI, Springer Verlag, pp. 243-254, 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018, Palma de Mallorce, Spain, 18/10/15. https://doi.org/10.1007/978-3-030-00202-2_20

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

Kanzawa, Yuchi ; Miyamoto, Sadaaki. / Generalized Fuzzy c-Means Clustering and Its Theoretical Properties. Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings. editor / Vicenc Torra ; Vicenc Torra ; Yasuo Narukawa ; Manuel González-Hidalgo ; Isabel Aguilo. Springer Verlag, 2018. pp. 243-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

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