Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models


 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 (EMSD) has recently shown attain higher accuracy compared other state-of-the-art generative for count clustering. Thus, in paper, we present Bayesian learning method based Markov Chain Monte Carlo and Metropolis-Hastings algorithm model parameters. This proposed validated via extensive simulations comparison multinomial models.