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Positive data clustering using finite inverted dirichlet mixture models

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Positive data clustering using finite inverted dirichlet mixture models

Bdiri, Taoufik (2010) Positive data clustering using finite inverted dirichlet mixture models. Masters thesis, Concordia University.

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Abstract

In this thesis we present an unsupervised algorithm for learning finite mixture models from multivariate positive data. Indeed, this kind of data appears naturally in many applications, yet it has not been adequately addressed in the past. This mixture model is based on the inverted Dirichlet distribution, which offers a good representation and modeling of positive non gaussian data. The proposed approach for estimating the parameters of an inverted Dirichlet mixture is based on the maximum likelihood (ML) using Newton Raphson method. We also develop an approach, based on the Minimum Message Length (MML) criterion, to select the optimal number of clusters to represent the data using such a mixture. Experimental results are presented using artificial histograms and real data sets. The challenging problem of software modules classification is investigated within the proposed statistical framework, also.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Bdiri, Taoufik
Pagination:viii, 42 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Institute for Information Systems Engineering
Date:2010
Thesis Supervisor(s):Bouguila, Nizar
ID Code:979480
Deposited By: Concordia University Library
Deposited On:09 Dec 2014 18:00
Last Modified:18 Jan 2018 17:49
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