Generalized Fuzzy c -Means Clustering and its Property of Fuzzy Classification Function

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...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics Vol. 25; no. 1; pp. 73 - 82
Main Authors: Kanzawa, Yuchi, Miyamoto, Sadaaki
Format: Journal Article
Language:English
Published: Tokyo Fuji Technology Press Co. Ltd 20.01.2021
Subjects:
Algorithms
Classification
Clustering
ISSN:1343-0130, 1883-8014
Online Access:Get full text
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Summary: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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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2021.p0073