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

被引：206
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
收藏
页码：80 / 116
页数：37
相关论文
共 122 条
45678910111213