Fuzzy set-based distant cluster identification

Authors:
Keon Myung Lee;Sun A. Lee
Affiliations:
Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, PT-ERC, Cheongju, Chungbuk, Republic of Korea;Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, PT-ERC, Cheongju, Chungbuk, Republic of Korea
Venue:
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Year:
2010

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Abstract

This presents a method to identify distant clusters for a data set using fuzzy techniques. The proposed method first applies a fuzzy clustering algorithm to the data set. It introduces a fuzzy membership function called distance membership function which transforms metric distance between two objects into the degree of farness. In order to measure the degree of farness between two fuzzy clusters, it defines the cluster farness measures which incorporate distance membership function and fuzzy clusters. It uses the measures to construct a fuzzy distant relation for clusters from which the distant clusters are identified.