Fuzzy Clustering Model using Principal Components Analysis and Naive Bayesian Classifier 


Vol. 11,  No. 4, pp. 485-490, Aug.  2004
10.3745/KIPSTB.2004.11.4.485


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  Abstract

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

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[IEEE Style]

S. H. Jun, "Fuzzy Clustering Model using Principal Components Analysis and Naive Bayesian Classifier," The KIPS Transactions:PartB , vol. 11, no. 4, pp. 485-490, 2004. DOI: 10.3745/KIPSTB.2004.11.4.485.

[ACM Style]

Sung Hae Jun. 2004. Fuzzy Clustering Model using Principal Components Analysis and Naive Bayesian Classifier. The KIPS Transactions:PartB , 11, 4, (2004), 485-490. DOI: 10.3745/KIPSTB.2004.11.4.485.

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