DocumentCode :
3187292
Title :
Research on Selection Method of the Optimal Weighting Exponent and Clustering Number in Fuzzy C-means Algorithm
Author :
Cui, Jian ; Li, Qiang ; Wang, Jun ; Zong, Da-Wei
Author_Institution :
Dept. of Early Warning Surveillance Intell., Wuhan Radar Inst., Wuhan, China
Volume :
3
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
104
Lastpage :
107
Abstract :
The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. The weighting exponent tn and the clustering number C are the important parameters in FCM algorithm. Conventional fuzzy clustering method must use both of the two prespecified parameters, so in this paper we analyses the original algorithm and studies on the optimal selection methods of the m and c by introducing the fuzzy decision theory and the validity index Vkwon basing on the geometric structure of the dataset. Experiment results with the IRIS dataset show that this algorithm can obtain the optimal weighting exponent m* and the optimal clustering number C*. Moreover, the fact that the best value scope of m achieved in practical applications indicates that the method is effective.
Keywords :
decision theory; fuzzy set theory; pattern clustering; IRIS dataset; clustering number; fuzzy c-mean algorithm; fuzzy decision theory; optimal weighting exponent; selection method; validity index; Algorithm design and analysis; Automation; Clustering algorithms; Clustering methods; Decision theory; Fuzzy set theory; Iris; Lakes; Radar; Surveillance; fuzzy decision theory; fuzzy-means; optimal clustering number; weighting exponent;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.411
Filename :
5522437
Link To Document :
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