INFORMATICA Informatica 1822-8844 0868-4952 0868-4952 Vilnius University Institute of Mathematics and Informatics Akademijos 4, LT-08663 Vilnius, Lithuania INFO585 10.15388/Informatica.2005.084 Research Article Multivariate Data Clustering for the Gaussian Mixture Model Kavaliauskas Mindaugas snaiperiui@takas.lt a Rudzkis Rimantas rudzkis@ktl.mii.lt b Faculty of Fundamental Science, Kaunas University of Technology, Donelaičio 72, LT-3000 Kaunas, Lithuania Department of Applied Statistics, Institute of Mathematics and Informatics, Akademijos 4, 08663 Vilnius, Lithuania 2005 16 1 61 74 1 6 2004 © 2005 Institute of Mathematics and Informatics, Vilnius 2005 Open access article under the CC BY license.

This paper discusses a soft sample clustering problem for multivariate independent random data satisfying the mixture model of the Gaussian distribution. The theory recommends to estimate the parameters of model by the maximum likelihood method and to use “plug-in” approach for data clustering. Unfortunately, the calculation problem of the maximum likelihood estimate is not completely solved in multivariate case. This work proposes a new constructive a few stage procedure to solve this task. This procedure includes statistical distribution analysis of a large number of the univariate projections of observations, geometric clustering of a multivariate sample and application of EM algorithm. The results of the accuracy analysis of the proposed methods is made by means of Monte-Carlo simulation.

 clustering multivariate data Gaussian mixture model projection-based clustering EM algorithm