DocumentCode :
3429332
Title :
Optimization of the number of clusters in fuzzy clustering
Author :
Liang, Zhehui ; Zhang, Pingjian ; Zhao, Juanjuan
Author_Institution :
Sch. of Comput. Sci. & Technol., South China Univ. of Technol., Guangzhou, China
Volume :
3
fYear :
2010
fDate :
25-27 June 2010
Abstract :
Clustering analysis is one of the most important research topics in unsupervised pattern recognition. Fuzzy clustering can reflect the real world more objectively since it establishes the uncertainty description from the sample to the classes. One of the main shortcomings of the traditional fuzzy clustering algorithm is that the number of clusters for reaching the optimal arrangement is not calculated automatically and needs user intervention. In this paper, by adopting the idea of hierarchical clustering, we propose a new adaptive fuzzy clustering algorithm A-FCM that can determine the optimal cluster number automatically and efficiently. Numerical experiments demonstrate that the A-FCM achieves better performance than other adaptive fuzzy clustering algorithms that determine the cluster number through various validity functions such as the Xie-Beni index.
Keywords :
fuzzy set theory; optimisation; pattern clustering; unsupervised learning; Xie-Beni index; cluster number; clustering analysis; fuzzy clustering; optimal arrangement; uncertainty description; unsupervised pattern recognition; Algorithm design and analysis; Clustering algorithms; Computer science; Design optimization; Geometry; Machine learning algorithms; Military computing; Pattern analysis; Pattern recognition; Uncertainty; clustering; fuzzy clustering; hierarchical clustering; number of clusters; validity function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Design and Applications (ICCDA), 2010 International Conference on
Conference_Location :
Qinhuangdao
Print_ISBN :
978-1-4244-7164-5
Electronic_ISBN :
978-1-4244-7164-5
Type :
conf
DOI :
10.1109/ICCDA.2010.5541372
Filename :
5541372
Link To Document :
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