In this paper, we examine deterministic and Bayesian methods for analyzing finite Dirichlet mixtures. The deterministic method is based on the likelihood approach, and the Bayesian approach is implemented using the Gibbs sampler. The selection of the number of clusters for both approaches is...

This paper presents an approach that partitions data sets of unlabeled binary vectors without a priori information about the number of clusters or the saliency of the features. The unsupervised binary feature selection problem is approached using finite mixture models of multivariate Bernoulli...