Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood

Authors:
Christophe Biernacki;Gilles Celeux;Gérard Govaert
Affiliations:
-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2000

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Abstract

We propose assessing a mixture model in a cluster analysis setting with the integrated completed likelihood. With this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the Integrated Completed Likelihood (ICL) is approximated using an à la Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of clusters leading to a sensible partitioning of the data.