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

Authors

  • xuanbo su Concordia Institute for Information Systems Engineering (CIISE), Concordia Uinversity, Montreal, QC, Canada
  • Nizar Bouguila
  • Nuha Zamzami

DOI:

https://doi.org/10.32473/flairs.v34i1.128506

Abstract

With the growth of social media information on the Web, performing clustering on different types of data is a challenging task.
Statistical approaches are widely used to tackle this task. Among the successful statistical approaches, finite mixture models have received a lot attention thanks to their flexibility. There are already many finite mixture models to cope with this task, but the Exponential Multinomial Scaled Dirichlet Distributions (EMSD) has recently shown to attain higher accuracy compared to other state-of-the-art generative models for count data clustering. Thus, in this paper, we present a Bayesian learning method based on Markov Chain Monte Carlo and Metropolis-Hastings algorithm for learning this model parameters. This proposed method is validated via extensive simulations and comparison with multinomial based mixture models.

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Published

2021-04-18

How to Cite

su, xuanbo, Bouguila, N., & Zamzami, N. (2021). Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128506

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Section

Main Track Proceedings