Exponential Family Mixed Membership Models for Soft Clustering of Multivariate Data

• Activity Number: 126 - Recent Advances in Bayesian Mixed Membership Modeling for Network, Longitudinal, and Multivariate Data
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Brendan Murphy* and Arthur White
• Companies: University College Dublin and Trinity College Dublin
• Keywords: Mixed Membership Models; Clustering; Soft Clustering; Mixture Models

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 and compared with a standard mixture model approach.