Classtering: Joint Classification And Clustering With Mixture Of Factor Analysers

In this work we propose a novel parametric Bayesian model for the problem of semi-supervised classification and clustering. Standard approaches of semi-supervised classification can recognize classes but cannot find groups of data. On the other hand, semi-supervised clustering techniques are able to discover groups of data but cannot find the associations between clusters and classes. The proposed model can classify and cluster samples simultaneously, allowing the analysis of data in the presence of an unknown number of classes and/or an arbitrary number of clusters per class. Experiments on synthetic and real world data show that the proposed model compares favourably to state-of-the-art approaches for semi-supervised clustering and that the discovered clusters can help to enhance classification performance, even in cases where the cluster and the low density separation assumptions do not hold. We finally show that when applied to a challenging real-world problem of subgroup discovery in breast cancer, the method is capable of maximally exploiting the limited information available and identifying highly promising subgroups.