Discrete data clustering using finite mixture models

2009 ◽  
Vol 42 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Nizar Bouguila ◽  
Walid ElGuebaly
Keyword(s):  
Mixture Models ◽  
Data Clustering ◽  
Finite Mixture Models ◽  
Discrete Data ◽  
Finite Mixture

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

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Xuanbo Su ◽  
Nizar Bouguila ◽  
Nuha Zamzami
Keyword(s):  
Mixture Models ◽  
Data Clustering ◽  
Bayesian Learning ◽  
Finite Mixture Models ◽  
State Of The Art ◽  
Generative Models ◽  
Finite Mixture ◽  
Model Parameters ◽  
Different Types ◽  
Statistical Approaches

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.

scholarly journals A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas
Keyword(s):  
Decision Making ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Risk Measures ◽  
Finite Mixture ◽  
Parametric Family ◽  
Collective Decision Making ◽  
Multiple Sources ◽  
Mixture Modelling ◽  
Expert Opinions

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

scholarly journals Non-local spatially varying finite mixture models for image segmentation

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Javier Juan-Albarracín ◽  
Elies Fuster-Garcia ◽  
Alfons Juan ◽  
Juan M. García-Gómez
Keyword(s):  
Image Segmentation ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Finite Mixture ◽  
Non Local ◽  
Spatially Varying

Finite Mixture Models

2013 ◽  
pp. 1296-1296
Author(s):  
Tracey Ward ◽  
Raphael Bernier ◽  
Cora Mukerji ◽  
Danielle Perszyk ◽  
James C. McPartland ◽  
...  
Keyword(s):  
Mixture Models ◽  
Finite Mixture Models ◽  
Finite Mixture
Sign in / Sign up

Export Citation Format

Share Document