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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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.
AbstractList 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.
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.
Author Miyamoto, Sadaaki
Kanzawa, Yuchi
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CorporateAuthor Shibaura Institute of Technology 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
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Snippet This study shows that a generalized fuzzy c -means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c -means clustering, can be...
This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c-means clustering, can be...
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SubjectTerms Algorithms
Classification
Clustering
Title Generalized Fuzzy c -Means Clustering and its Property of Fuzzy Classification Function
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