DocumentCode
2748401
Title
Parameter optimization in FCM clustering algorithms
Author
Xinbo, Gao ; Jie, LI ; Weixin, Xie
Author_Institution
Sch. of Electron. Eng., Xidian Univ., Xi´´an, China
Volume
3
fYear
2000
fDate
2000
Firstpage
1457
Abstract
Weighting exponent m is an important parameter in fuzzy c-means (FCM) clustering algorithm, which directly affects the performance of the algorithm and the validity of fuzzy cluster analysis. However, so far the optimal choice of m is still an open problem. A method of selecting the optimal m is proposed in this paper, which is based on the fuzzy decision theory. The experimental results obtained demonstrate its effectiveness and arrive a conclusion that the optimal range of m is [1.5, 2.5] in practical applications
Keywords
decision theory; fuzzy set theory; optimisation; pattern clustering; fuzzy c-means clustering; fuzzy decision theory; parameter optimization; pattern recognition; weighting exponent; Algorithm design and analysis; Clustering algorithms; Decision theory; Entropy; Fuzzy control; Fuzzy set theory; Fuzzy sets; Partitioning algorithms; Pattern recognition; Performance analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-5747-7
Type
conf
DOI
10.1109/ICOSP.2000.893376
Filename
893376
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