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

Authors: 

Marco Bertoletti, Nial Friel, Riccardo Rastelli

Publication Type: 
Refereed Original Article
Abstract: 
Theintegratedcompletedlikelihood(ICL)criterionhasproventobeaverypopular 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.
Digital Object Identifer (DOI): 
10.1007/s40300-015-0064-5
Publication Status: 
Published
Publication Date: 
19/05/2015
Journal: 
METRON
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No