Finite mixture models and model-based clusteringFinite mixture models and model-based clustering

被引：209

作者：

Melnykov, Volodymyr ^{[1
]}

Maitra, Ranjan ^{[2
]}

机构：

[1] North Dakota State Univ, Dept Stat, Fargo, ND 58105 USA

[2] Iowa State Univ, Dept Stat, Ames, IA 50011 USA

来源：

STATISTICS SURVEYS | 2010年 / 4卷

基金：

美国国家科学基金会;

关键词：

EM algorithm; model selection; variable selection; diagnostics; two-dimensional gel electrophoresis data; proteomics; text mining; magnitude magnetic resonance image;

D O I：

10.1214/09-SS053

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：

Finite mixture models have a long history in statistics, having been used to model population heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classification. This paper provides a detailed review into mixture models and model-based clustering. Recent trends as well as open problems in the area are also discussed.

引用

页码：80 / 116

页数：37

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