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http://dx.doi.org/10.5391/JKIIS.2008.18.3.354

Clustering Method for Reduction of Cluster Center Distortion  

Jeong, Hye-C. (Dept. of Electrical Engineering, Yeungnam University)
Seo, Suk-T. (Dept. of Electrical Engineering, Yeungnam University)
Lee, In-K. (Dept. of Electrical Engineering, Yeungnam University)
Kwon, Soon-H. (Dept. of Electrical Engineering, Yeungnam University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.3, 2008 , pp. 354-359 More about this Journal
Abstract
Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.
Keywords
Cluster; center distortion; FCM; Nearest data; Data weight;
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