title: Improved initialisation of model-based clustering using Gaussian hierarchical partitions.
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.
authors.affiliation: Dipartimento di Economia, Università degli Studi di Perugia, Via A. Pascoli 20, 06123 Perugia (Italy).
authors.forename: Luca
authors.initials: L
authors.lastname: Scrucca
authors.affiliation: Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195-4322.
authors.forename: Adrian E
authors.initials: AE
authors.lastname: Raftery
keywords: Model-based clustering; data transformation; mclust; model-based agglomerative hierarchical clustering