期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 39, 期 3, 页码 578-591出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2008.2004818
关键词
Clustering algorithm; competitive learning; fuzzy partitions; membership constraint function
类别
资金
- Hong Kong Polytechnic University [Z-08R, G-U296]
- National 973 Key Project [2006CB705700]
- National Science Foundation of China [60773206, 60704047]
- National 863 Research [2007AAlZ158]
- Ministry of Education of China [A 1420461266, NCET-04-0496]
- National KeySoft Laboratory
- National Key Laboratory of CAD
- Key Laboratory of Computer Information Technologies
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.
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