  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.

Methodology