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Math. Comput. Appl. 2016, 21(3), 34; doi:10.3390/mca21030034

A Comparison of Information Criteria in Clustering Based on Mixture of Multivariate Normal Distributions

1
Department of Statistics, Yildiz Technical University, Istanbul 34220, Turkey
2
Department of Statistics, Necmettin Erbakan University, Konya 42090, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Mehmet Ilgın
Received: 3 June 2016 / Revised: 13 July 2016 / Accepted: 22 July 2016 / Published: 1 August 2016
View Full-Text   |   Download PDF [545 KB, uploaded 1 August 2016]   |  

Abstract

Clustering analysis based on a mixture of multivariate normal distributions is commonly used in the clustering of multidimensional data sets. Model selection is one of the most important problems in mixture cluster analysis based on the mixture of multivariate normal distributions. Model selection involves the determination of the number of components (clusters) and the selection of an appropriate covariance structure in the mixture cluster analysis. In this study, the efficiency of information criteria that are commonly used in model selection is examined. The effectiveness of information criteria has been determined according to the success in the selection of the number of components and in the selection of an appropriate covariance matrix. View Full-Text
Keywords: cluster analysis; mixture models; information criteria cluster analysis; mixture models; information criteria
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Akogul, S.; Erisoglu, M. A Comparison of Information Criteria in Clustering Based on Mixture of Multivariate Normal Distributions. Math. Comput. Appl. 2016, 21, 34.

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