Assessing a mixture model for clustering with the integrated completed likelihood

An assessing method of mixture model in a cluster analysis setting with integrated completed likelihood appears to be more robust to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.

Abstract

We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For 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 the 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 dusters leading to a sensible partitioning of the data.