Improved initialisation of model-based clustering using Gaussian hierarchical partitions

Luca Scrucca and Adrian Raftery ()

Advances in Data Analysis and Classification, 2015, vol. 9, issue 4, 447-460

Abstract: Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. This choice is computationally convenient and often yields good clustering partitions. However, in certain circumstances, poor initial partitions may cause the EM algorithm to converge to a local maximum of the likelihood function. We propose several simple and fast refinements based on data transformations and illustrate them through data examples. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Model-based clustering, Model-based agglomerative hierarchical clustering, mclust , Data transformation, 62H30 (Classification and discrimination; cluster analysis), (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11634-015-0220-z

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