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Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models

P. McNicholas,T. B. Murphy,Aaron F. McDaid,Dermot Frost

Published 2010 in Computational Statistics & Data Analysis

ABSTRACT

Model-based clustering using a family of Gaussian mixture models, with parsimonious factor analysis-like covariance structure, is described and an ecient algorithm for its implementation is presented. This algorithm uses the alternating expectationconditional maximization (AECM) variant of the expectation-maximization (EM) algorithm. Two central issues around the implementation of this family of models, namely model selection and convergence criteria, are discussed. These central issues also have implications for other model-based clustering techniques and for the implementation of techniques like the EM algorithm, in general. The Bayesian information criterion (BIC) is used for model selection and Aitken’s acceleration, which is shown to outperform the lack of progress criterion, is used to determine convergence. A brief introduction to parallel computing is then given before the implementation of this algorithm in parallel is facilitated within the master-slave paradigm. A simulation study is then carried out to confirm the eectiveness of this parallelization. The resulting software is applied to two data sets to demonstrate its eectiveness when compared to existing software.

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