Generalized fuzzy c-means clustering and its property of fuzzy classification function

Yuchi Kanzawa; Sadaaki Miyamoto

doi:10.20965/JACIII.2021.P0073

Generalized fuzzy c-means clustering and its property of fuzzy classification function

Yuchi Kanzawa, Sadaaki Miyamoto

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential 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 a behavior similar to that of both standard and exponential fuzzy c-means clustering.

Original language	English
Pages (from-to)	73-82
Number of pages	10
Journal	Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume	25
Issue number	1
DOIs	https://doi.org/10.20965/JACIII.2021.P0073
Publication status	Published - 2021 Jan 20

Keywords

Fuzzy c-means clustering
Fuzzy classification function

ASJC Scopus subject areas

Human-Computer Interaction
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.20965/JACIII.2021.P0073

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AB - This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential 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 a behavior similar to that of both standard and exponential fuzzy c-means clustering.

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KW - Fuzzy classification function

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Generalized fuzzy c-means clustering and its property of fuzzy classification function

Abstract

Keywords

ASJC Scopus subject areas

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