NDLI: On Fuzzy Clustering Based Self-Organized Methods

Content Provider	IEEE Xplore Digital Library
Author	Sato-Ilic, M. Kuwata, T.
Copyright Year	2005
Description	Author affiliation: Fac. of Syst. & Inf. Eng., Tsukuba Univ. (Sato-Ilic, M.; Kuwata, T.)
Abstract	This paper presents two methods based on self-organized dissimilarity. The first is an implemented fuzzy clustering and the second is a hybrid method of fuzzy clustering and multidimensional scaling (MDS). Specifically, a self-organized dissimilarity is defined that uses the result of fuzzy clustering in such a way that the dissimilarity of objects is influenced by the dissimilarity of the classification situations corresponding to the objects. In other words, the dissimilarity is defined under an assumption that similar objects have similar classification structures. Through empirical evaluation the proportion and the fitness of the results of the method, which uses MDS combined with fuzzy clustering, is shown to be effective in real data. Furthermore, by exploiting the self-organized similarity, defuzzification of fuzzy clustering can cope with the inherent classification structures
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	973
Ending Page	978
File Size	1970896
Page Count	6
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452526
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Multidimensional systems Systems engineering and theory Fuzzy logic Neural networks Optimization methods Extraterrestrial measurements Clustering methods
Content Type	Text
Resource Type	Article

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