TY - GEN
T1 - Generalized Fuzzy c-Means Clustering and Its Theoretical Properties
AU - Kanzawa, Yuchi
AU - Miyamoto, Sadaaki
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers standard 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 similar behavior to that of standard fuzzy c-means clustering.
AB - This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers standard 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 similar behavior to that of standard fuzzy c-means clustering.
KW - Fuzzy c-means clustering
KW - Fuzzy classification function
UR - http://www.scopus.com/inward/record.url?scp=85055721268&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055721268&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00202-2_20
DO - 10.1007/978-3-030-00202-2_20
M3 - Conference contribution
AN - SCOPUS:85055721268
SN - 9783030002015
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 243
EP - 254
BT - Modeling Decisions for Artificial Intelligence - 15th International Conference, MDAI 2018, Proceedings
A2 - Torra, Vicenc
A2 - Torra, Vicenc
A2 - Narukawa, Yasuo
A2 - González-Hidalgo, Manuel
A2 - Aguilo, Isabel
PB - Springer Verlag
T2 - 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018
Y2 - 15 October 2018 through 18 October 2018
ER -