DocumentCode
2001859
Title
FNM-based and RFCM-based fuzzy clustering for tri-relational data
Author
Kanzawa, Yuchi
Author_Institution
Shibaura Inst. of Technol., Tokyo, Japan
fYear
2012
fDate
20-24 Nov. 2012
Firstpage
1982
Lastpage
1987
Abstract
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.
Keywords
entropy; fuzzy set theory; numerical analysis; pattern clustering; relational databases; FNM-based fuzzy clustering; RFCM-based fuzzy clustering; data points; entropy-regularization; fuzzy nonmetric model; relational fuzzy c-means; standard regularization; trirelational data;
fLanguage
English
Publisher
ieee
Conference_Titel
Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on
Conference_Location
Kobe
Print_ISBN
978-1-4673-2742-8
Type
conf
DOI
10.1109/SCIS-ISIS.2012.6505050
Filename
6505050
Link To Document