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

被引：0

作者：

Kim, Seung-Gu ^{[1
]}

机构：

[1] Sangji Univ, Dept Data & Informat, 83 Usan Dong, Wonju 220702, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2013年 / 26卷 / 05期

关键词：

Saliency parameter; variable selection; clustering; normal mixture model; EM algorithm;

D O I：

10.5351/KJAS.2013.26.5.821

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 and 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.

引用

页码：821 / 834

页数：14

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