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Author Dian-Rong Yang ♦ Leu-Shing Lan ♦ Shih-Hung Liao
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Fuzzy control ♦ Clustering methods ♦ Clustering algorithms ♦ Partitioning algorithms ♦ Entropy ♦ Algorithm design and analysis ♦ Convergence
Abstract Clustering is an unsupervised procedure to group objects in accordance with their similarities. For non-separable clusters, the concept of fuzziness is incorporated. Among other approaches, the fuzzy c-means algorithm is the most well-known fuzzy clustering method. In this work, we present a modified form of the fuzzy c-means based on a new definition of distance measure which can be considered as an extension of the conventional one. The key advantage of this new fuzzy clustering scheme is its ability to flexibly control the membership function curves. Analytical formulae have been derived for both cluster centers and the fuzzy partition matrix. Parameter effects related to the membership function curves have also been analyzed. Examples are given to demonstrate the clustering results of the newly presented scheme.
Description Author affiliation: Nat. Yunlin Univ. of Sci. & Technol., Douliou (Dian-Rong Yang; Leu-Shing Lan; Shih-Hung Liao)
ISBN 0780394887
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-07-16
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 821.45 kB
Page Count 4
Starting Page 952
Ending Page 955

Source: IEEE Xplore Digital Library