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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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 25; H. 1; S. 73 - 82
Hauptverfasser: Kanzawa, Yuchi, Miyamoto, Sadaaki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 20.01.2021
Schlagworte:
Algorithms
Classification
Clustering
ISSN:1343-0130, 1883-8014
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2021.p0073