In this paper we present the finite mixture models approach to clustering of high dimensional data. The mixture resolving approach to cluster analysis has been addressed in a number of different ways; the underlying assumption is that the patterns to be clustered are drawn from one of several distributions, and the goal is to identify the parameters of each and (perhaps) their number. Finite mi...

In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter sparsity is substantial. Classification with finite mixture models is based on the posterior expectation of latent indicator variables. These quantities are typically estimated using the expectation-maximization (EM) alg...

BACKGROUND . When data on preferences are not available, analysts rely on condition-specific or generic measures of health status like the SF-12 for predicting or mapping preferences. Such prediction is challenging because of the characteristics of preference data, which are bounded, have multiple modes, and have a large proportion of observations clustered at values of 1. METHODS . We develo...

This work investigates a combined stochastic and deterministic optimization approach for multivariate mixture density estimation. Mixture probability density models are selected and optimized by combining the optimization characteristics of a multiagent stochastic optimization algorithm based on evolutionary programming and the expectation-maximization algorithm. Unlike the traditional finite m...

This article creates a general class of perturbation models which are described by an underlying null model that accounts for most of the structure in data while a perturbation accounts for possible small localized departures. The goal is to develop theory and inferential methods for fitting the perturbation models including the general case when the null model contains a set of nuisance parame...


 With the growth of social media information on Web, performing clustering different types data is a challenging task.Statistical approaches are widely used to tackle this task. Among successful statistical approaches, finite mixture models have received lot attention thanks their flexibility. There already many cope with task, but Exponential Multinomial Scaled Dirichlet Distributions (E...