Finite mixture models and model-based clustering

IF 11 Q1 STATISTICS & PROBABILITY

Statistics Surveys Pub Date : 2010-01-01 DOI:10.1214/09-SS053

Volodymyr Melnykov, R. Maitra

引用次数: 262

Abstract

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

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有限混合模型和基于模型的聚类

有限混合模型在统计学中有着悠久的历史，已经被用来模拟人口异质性，推广分布假设，最近，为聚类和分类提供了一个方便而正式的框架。本文详细介绍了混合模型和基于模型的聚类。本文还讨论了该领域的最新发展趋势以及有待解决的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics Surveys STATISTICS & PROBABILITY-

CiteScore

11.70

自引率

0.00%

发文量

期刊介绍： Statistics Surveys publishes survey articles in theoretical, computational, and applied statistics. The style of articles may range from reviews of recent research to graduate textbook exposition. Articles may be broad or narrow in scope. The essential requirements are a well specified topic and target audience, together with clear exposition. Statistics Surveys is sponsored by the American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and by the Statistical Society of Canada.