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http://dx.doi.org/10.5351/KJAS.2013.26.5.821

Variable Selection in Clustering by Recursive Fit of Normal Distribution-based Salient Mixture Model  

Kim, Seung-Gu (Department of Data and Information, Sangji University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.5, 2013 , pp. 821-834 More about this Journal
Abstract
Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.
Keywords
Saliency parameter; variable selection; clustering; normal mixture model; EM algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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