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Exponential family mixed membership models for soft clustering of multivariate data

Author

Listed:
  • Arthur White

    (Trinity College Dublin, The University of Dublin)

  • Thomas Brendan Murphy

    (University College Dublin)

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.

Suggested Citation

  • Arthur White & Thomas Brendan Murphy, 2016. "Exponential family mixed membership models for soft clustering of multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 521-540, December.
    • Handle: RePEc:spr:advdac:v:10:y:2016:i:4:d:10.1007_s11634-016-0267-5
    DOI: 10.1007/s11634-016-0267-5
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    References listed on IDEAS

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    1. Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
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