The Dirichlet Process mixture (DPM) model is a popular nonparametric Bayesian tool for modeling unknown distributions through mixtures of components. Integrating out the latent location variables in a DPM model leads to a product partition model. This paper describes a modefinding algorithm which quickly finds either the maximizer of the partition posterior or the maximizer of the partition lik...

We introduce a flexible class of mixture models for clustering and density estimation. Our model allows clustering of non-linearly-separable data, produces a potentially low-dimensional latent representation, automatically infers the number of clusters, and produces a density estimate. Our approach makes use of two tools from Bayesian nonparametrics: a Dirichlet process mixture model to allow a...

Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational interest and content preference simultaneously. In this paper we will present a novel web co-clust...

Hierarchical latent class (HLC) models generalize latent class models. As models for cluster analysis, they suit more applications than the latter because they relax the often untrue conditional independence assumption. They also facilitate the discovery of latent causal structures and the induction of probabilistic models that capture complex dependencies and yet have low inferential complexit...

A mixture approach to clustering is an important technique in cluster analysis. A mixture of multivariate multinomial distributions is usually used to analyze categorical data with latent class model. The parameter estimation is an important step for a mixture distribution. Described here are four approaches to estimating the parameters of a mixture of multivariate multinomial distributions. Th...

Background: Metabolic syndrome (MetS), a combination of coronary heart disease and diabetes mellitus risk factor, refer to one of the most challenging public health issues in worldwide. The aim of this study was to identify the subgroups of participants in a study on the basis of MetS components. Methods: The cross-sectional study took place in the districts related to Tehran University of Medi...

AIMS Examining the co-occurrence patterns of modifiable biobehavioral risk factors for deadly chronic diseases (e.g. cancer, cardiovascular disease, diabetes) can elucidate the etiology of risk factors and guide disease-prevention programming. The aims of this study were to (1) identify latent classes based on the clustering of five key biobehavioral risk factors among US adults who reported at...