Advances in Robust Constrained Model Based Clustering

Model-based approaches to cluster analysis and mixture modelling often involve maximizing classification and mixture likelihoods. Robust clustering and mixture modelling procedures, that can resist certain amount of contaminating data, can be introduced by considering trimmed versions of those classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these trimmed likelihood maximizations result in ill-posed problems. Moreover, non-interesting or “spurious” clusters are often detected by unconstrained algorithms aimed at maximizing these trimmed likelihood criteria.