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Initialisation of the EM algorithm in model-based clustering is oftencrucial. Various starting points in the parameter space often lead to differentlocal maxima of the likelihood function and, so to different clusteringpartitions. Among the several approaches available in the literature,model-based agglomerative hierarchical clustering is used to provide initialpartitions in the popular MCLUST R package. This choice is computationallyconvenient and often yields good clustering partitions. However, in certaincircumstances, poor initial partitions may cause the EM algorithm to convergeto a local maximum of the likelihood function. We propose several simple andfast refinements based on data transformations and illustrate them through dataexamples.

Improved initialisation of model-based clustering using Gaussian hierarchical partitions