NDLI: Swarm Based Fuzzy Clustering with Partition Validity

Content Provider	IEEE Xplore Digital Library
Author	Hall, L.O. Kanade, P.M.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., South Florida Univ., Tampa, FL (Hall, L.O.; Kanade, P.M.)
Abstract	Swarm based approaches to clustering have been shown to be able to skip local extrema by doing a form of global search. We previously reported on the use of a swarm based approach using artificial ants to do fuzzy clustering by optimizing the fuzzy c-means (FCM) criterion. FCM requires that one choose the number of cluster centers (c). In the event that the user of the algorithm is unsure of the number of cluster centers, they can try several different choices and evaluate them with a cluster validity metric. In this work, we use a fuzzy cluster validity metric proposed by Xie and Beni as the criterion for evaluating a partition produced by swarm based clustering. Interestingly, when provided with more clusters than exist in the data our ant-based approach produces a partition with empty clusters and/or very lightly populated clusters. We used two data sets, Iris and an artificially generated data set, to show that optimizing a cluster validity metric with a swarm based approach can effectively provide an indication of how many clusters there are in the data
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	991
Ending Page	995
File Size	1936792
Page Count	5
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452529
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	Clustering algorithms Partitioning algorithms Iris Computer science Fuzzy logic Prototypes
Content Type	Text
Resource Type	Article

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