Work

Finite mixture models and model-based clustering

Year: 2010

Type: article

Abstract: 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.

Source: Statistics Surveys

DOI: 10.1214/09-ss053

Authors: Volodymyr Melnykov, Ranjan Maitra

Institutions: Iowa State University, North Dakota State University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: CAREER: Methodology for Statistical Computing in Massive Datasets: Parallel Approaches to Cluster and MCMC Estimation, G6671297155