TY - JOUR
T1 - Generalized fuzzy c-means clustering and its property of fuzzy classification function
AU - Kanzawa, Yuchi
AU - Miyamoto, Sadaaki
N1 - Publisher Copyright:
© 2021 Fuji Technology Press. All rights reserved.
PY - 2021/1/20
Y1 - 2021/1/20
N2 - 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.
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.
KW - Fuzzy c-means clustering
KW - Fuzzy classification function
UR - http://www.scopus.com/inward/record.url?scp=85100508548&partnerID=8YFLogxK
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U2 - 10.20965/JACIII.2021.P0073
DO - 10.20965/JACIII.2021.P0073
M3 - Article
AN - SCOPUS:85100508548
SN - 1343-0130
VL - 25
SP - 73
EP - 82
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
IS - 1
ER -