Title :
FNM-based and RFCM-based fuzzy clustering for tri-relational data
Author_Institution :
Shibaura Inst. of Technol., Tokyo, Japan
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;
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
DOI :
10.1109/SCIS-ISIS.2012.6505050
