Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31208
Title: Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
Authors: Zhu, L
Chung, FL 
Wang, S
Keywords: Clustering algorithm
Competitive learning
Fuzzy partitions
Membership constraint function
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2009, v. 39, no. 3, p. 578-591 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms, and it should not be forced to fix at the usual value m = 2. In view of its distinctive features in applications and its limitation in having m = 2 only, a recent advance of fuzzy clustering called fuzzy c-means clustering with improved fuzzy partitions (IFP-FCM) is extended in this paper, and a generalized algorithm called GIFP-FCM for more effective clustering is proposed. By introducing a novel membership constraint function, a new objective function is constructed, and furthermore, GIFP-FCM clustering is derived. Meanwhile, from the viewpoints of L p norm distance measure and competitive learning, the robustness and convergence of the proposed algorithm are analyzed. Furthermore, the classical fuzzy c-means algorithm (FCM) and IFP-FCM can be taken as two special cases of the proposed algorithm. Several experimental results including its application to noisy image texture segmentation are presented to demonstrate its average advantage over FCM and IFP-FCM in both clustering and robustness capabilities.
URI: http://hdl.handle.net/10397/31208
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2008.2004818
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