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Computational Intelligence and Neuroscience
Volume 2014, Article ID 368628, 10 pages
Research Article

An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

1School of Mechanical Engineering, Tongji University, Shanghai 200092, China
2Precision Medical Device Department, University of Shanghai for Science and Technology, Shanghai 200093, China

Received 8 June 2014; Revised 30 September 2014; Accepted 14 October 2014; Published 12 November 2014

Academic Editor: Daoqiang Zhang

Copyright © 2014 Jian Zhang and Ling Shen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy -means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.