Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion

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Title: Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion
Authors: Bertoletti, Marco
Friel, Nial
Rastelli, Riccardo
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Date: Aug-2015
Online since: 2017-04-18T14:31:51Z
Abstract: The integrated completed likelihood (ICL) criterion has proven to be a very popular approach in model-based clustering through automatically choosing the number of clusters in a mixture model. This approach effectively maximises the complete data likelihood, thereby including the allocation of observations to clusters in the model selection criterion. However for practical implementation one needs to introduce an approximation in order to estimate the ICL. Our contribution here is to illustrate that through the use of conjugate priors one can derive an exact expression for ICL and so avoiding any approximation. Moreover, we illustrate how one can find both the number of clusters and the best allocation of observations in one algorithmic framework. The performance of our algorithm is presented on several simulated and real examples.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Metron
Volume: 73
Issue: 2
Start page: 177
End page: 199
Copyright (published version): 2015 Sapienza Universit√† di Roma
Keywords: Machine Learning & StatisticsIntegrated completed likelihoodFinite mixture modelsModel-based clusteringGreedy search
DOI: 10.1007/s40300-015-0064-5
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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