Exponential family mixed membership models for soft clustering of multivariate data
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...
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Veröffentlicht in: | Advances in data analysis and classification 2016-12, Vol.10 (4), p.521-540 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 1862-5347 1862-5355 |
DOI: | 10.1007/s11634-016-0267-5 |