Volume 6 Number 5 (May 2011)
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JSW 2011 Vol.6(5): 873-879 ISSN: 1796-217X
doi: 10.4304/jsw.6.5.873-879

An Improved Fuzzy C-means Clustering Algorithm based on PSO

Qiang Niu, Xinjian Huang
School of Computer Science &Technology China University of Mining & Technology Xuzhou Jiangsu, China

Abstract—To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership.

Index Terms—clustering, particle swarm algorithm, fuzzy C means, membership, constraint strategy


Cite: Qiang Niu, Xinjian Huang, "An Improved Fuzzy C-means Clustering Algorithm based on PSO," Journal of Software vol. 6, no. 5, pp. 873-879, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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