Exponential family mixed membership models for soft clustering of multivariate data

Arthur White () and Thomas Brendan Murphy
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Arthur White: Trinity College Dublin, The University of Dublin
Thomas Brendan Murphy: University College Dublin

Advances in Data Analysis and Classification, 2016, vol. 10, issue 4, No 6, 540 pages

Abstract: Abstract For several years, model-based clustering methods have successfully tackled many of the challenges presented by data-analysts. However, as the scope of data analysis has evolved, some problems may be beyond the standard mixture model framework. One such problem is when observations in a dataset come from overlapping clusters, whereby different clusters will possess similar parameters for multiple variables. In this setting, mixed membership models, a soft clustering approach whereby observations are not restricted to single cluster membership, have proved to be an effective tool. In this paper, a method for fitting mixed membership models to data generated by a member of an exponential family is outlined. The method is applied to count data obtained from an ultra running competition, and compared with a standard mixture model approach.

Keywords: Mixed membership models; Model based clustering; Mixture models; Variational Bayes; 62H30 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11634-016-0267-5

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