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Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
July 2000 (vol. 22 no. 7)
pp. 719-725

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.

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Index Terms:
Mixture model, clustering, integrated likelihood, BIC, integrated completed likelihood, ICL criterion.
Citation:
Christophe Biernacki, Gilles Celeux, Gérard Govaert, "Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 7, pp. 719-725, July 2000, doi:10.1109/34.865189
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