TY - GEN
T1 - FNM-based and RFCM-based fuzzy clustering for tri-relational data
AU - Kanzawa, Yuchi
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper, some fuzzy clustering methods are proposed for relational data which represents the dissimilarity for triples of data points. One method is based on the fuzzy nonmetric model and the other is on the relational fuzzy c-means. Each method has two options of fuzzification; the standard and the entropy-regularization. Through some numerical experiments, the feature of the proposed methods is discussed.
AB - In this paper, some fuzzy clustering methods are proposed for relational data which represents the dissimilarity for triples of data points. One method is based on the fuzzy nonmetric model and the other is on the relational fuzzy c-means. Each method has two options of fuzzification; the standard and the entropy-regularization. Through some numerical experiments, the feature of the proposed methods is discussed.
UR - http://www.scopus.com/inward/record.url?scp=84877796642&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877796642&partnerID=8YFLogxK
U2 - 10.1109/SCIS-ISIS.2012.6505050
DO - 10.1109/SCIS-ISIS.2012.6505050
M3 - Conference contribution
AN - SCOPUS:84877796642
SN - 9781467327428
T3 - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
SP - 1982
EP - 1987
BT - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
T2 - 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
Y2 - 20 November 2012 through 24 November 2012
ER -