Hu, Can, Fan, Wentao, Du, JiXiang and Bouguila, Nizar ORCID: https://orcid.org/0000000172247940 (2018) A Novel Statistical Approach for Clustering Positive Data Based on Finite Inverted BetaLiouville Mixture Models. Neurocomputing . ISSN 09252312 (In Press)
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Official URL: http://dx.doi.org/10.1016/j.neucom.2018.12.066
Abstract
Nowadays, a great number of positive data has been occurred naturally in many applications, however, it was not adequately analyzed. In this article, we propose a novel statistical approach for clustering multivariate positive data. Our approach is based on a finite mixture model of inverted BetaLiouville (IBL) distributions, which is proper choice for modeling and analysis of positive vector data. We develop two different approaches to learn the proposed mixture model. Firstly, the maximum likelihood (ML) is utilized to estimate parameters of the finite inverted BetaLiouville mixture model in which the right number of mixture components is determined according to the minimum message length (MML) criterion. Secondly, the variational Bayes (VB) is adopted to learn our model where the parameters and the number of mixture components can be determined simultaneously in a unified framework, without the requirement of using information criteria. We investigate the effectiveness of our model by conducting a series of experiments on both synthetic and real data sets.
Divisions:  Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering 

Item Type:  Article 
Refereed:  Yes 
Authors:  Hu, Can and Fan, Wentao and Du, JiXiang and Bouguila, Nizar 
Journal or Publication:  Neurocomputing 
Date:  30 December 2018 
Funders: 

Digital Object Identifier (DOI):  10.1016/j.neucom.2018.12.066 
Keywords:  Clustering; Mixture models; Variational Bayes; Maximum likelihood; Minimum message; length; Inverted BetaLiouville 
ID Code:  984850 
Deposited By:  MICHAEL BIRON 
Deposited On:  10 Jan 2019 14:47 
Last Modified:  10 Jan 2019 14:47 
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