Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions

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...

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Vydané v:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Ročník 39; číslo 3; s. 578 - 591
Hlavní autori: Zhu, Lin, Chung, Fu-Lai, Wang, Shitong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2009
Predmet:
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithm
Clustering algorithms
competitive learning
Computer science education
Convergence
Cybernetics
Fuzzy
Fuzzy logic
fuzzy partitions
Fuzzy set theory
Image texture
Information technology
Laboratories
membership constraint function
Partitioning algorithms
Pattern recognition
Robustness
Texture
ISSN:1083-4419, 1941-0492, 1941-0492
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2008.2004818