Fuzzy structural classification methods

Authors:
Mika Sato-Ilic;Tomoyuki Kuwata
Affiliations:
Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan;Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

Citing 3
Cited 1

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Abstract

This paper presents several fuzzy clustering methods based on self-organized similarity (or dissimilarity). Self-organized similarity (or dissimilarity) has been proposed in order to consider not only the similarity (or dissimilarity) between a pair of objects but also the similarity (or dissimilarity) between the classification structures of objects. Depending on how the similarity (or dissimilarity) of the classification structures cope with the fuzzy clustering methods, the results will be different from each other. This paper discusses this difference and shows several numerical examples.