DocumentCode :
2663461
Title :
A Probabilistic Cluster Validity Index for Agglomerative Bayesian Fuzzy Clustering
Author :
Lee, Sang Wan ; Kim, Yong Soo ; Bien, Zeungnam
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Korean Adv. Inst. of Sci. & Technol., Daejeon, South Korea
fYear :
2008
fDate :
10-12 Dec. 2008
Firstpage :
368
Lastpage :
373
Abstract :
A novel fuzzy clustering technique, called Agglomerative Iterative Bayesian Fuzzy Clustering (IBFC) with a novel cluster validity index is presented. The algorithm has a fuzzy competitive learning structure properly incorporated with Bayesian decision rule. Based on this Bayesian assumption, we propose a probabilistic cluster validity index, by which an optimal number of clusters is determined. We reports that the proposed algorithm shows better performance when tested with synthetic/benchmark data and compared with several well-known methods.
Keywords :
Bayes methods; fuzzy set theory; pattern clustering; Bayesian assumption; Bayesian decision rule; agglomerative Bayesian fuzzy clustering; fuzzy competitive learning structure; probabilistic cluster validity index; Bayesian methods; Benchmark testing; Clustering algorithms; Euclidean distance; Iterative algorithms; Partitioning algorithms; Phase change materials; Prototypes; Pursuit algorithms; Shape measurement; Bayesian Fuzzy Clustering; Cluster Validity Index; Fuzzy Clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-0-7695-3514-2
Type :
conf
DOI :
10.1109/CIMCA.2008.76
Filename :
5172653
Link To Document :
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