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Finite mixture models and model-based clustering

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Author(s): Volodymyr Melnykov | Ranjan Maitra

Journal: Statistics Surveys
ISSN 1935-7516

Volume: 4;
Start page: 80;
Date: 2010;
Original page

Keywords: EM algorithm | Model selection | Variable selection | Diagnostics | Two-dimensional gel electrophoresis data | Proteomics | Text mining | Magnitude magnetic resonance images

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.
RPA Switzerland

Robotic Process Automation Switzerland

    

Tango Jona
Tangokurs Rapperswil-Jona