Exponential family mixed membership models for soft clustering of multivariate data

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Title: Exponential family mixed membership models for soft clustering of multivariate data
Authors: White, ArthurMurphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/7981
Date: Dec-2016
Online since: 2017-08-09T01:00:13Z
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Advances in Data Analysis and Classification
Volume: 10
Issue: 4
Start page: 521
End page: 540
Copyright (published version): 2016 Springer-Verlag Berlin Heidelberg
Keywords: Machine learningStatisticsMixed membership modelsModel based clusteringMixture modelsVariational Bayes
DOI: 10.1007/s11634-016-0267-5
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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