Clustering based on Dirichlet mixtures of attribute ensembles

We propose a model-based approach to identifying clusters of objects based on subsets of attributes, so that the attributes that distinguish a cluster from the rest of the population, called an attribute ensemble, may depend on the cluster being considered. The model is based on a Pólya urn cluster model, which is equivalent to a Dirichlet process mixture of multivariate normal distributions. This model-based approach allows for the incorporation of applicationspecific data features into the clustering scheme. For example, in an analysis of genetic CGH array data we account for spatial correlation of genetic abnormalities along the genome. Some key words: nonparametric Bayes, unsupervised learning, subspace clustering, variable selection, COSA.