Abstract Model-based clustering based on a finite mixture of Gaussian components is an effective method for looking for groups of observations in a dataset. In this paper we propose a dimension reduction method, called MCLUSTSIR, which is able to show clustering structures depending on the selected Gaussian mixture model. The method aims at finding those directions which are able to display both variation in cluster means and variations in cluster covariances. The resulting MCLUSTSIR variables are defined as a linear mapping method which projects the data onto a suitable subspace.

Visualization of model-based clustering structures

SCRUCCA, Luca
2010

Abstract

Abstract Model-based clustering based on a finite mixture of Gaussian components is an effective method for looking for groups of observations in a dataset. In this paper we propose a dimension reduction method, called MCLUSTSIR, which is able to show clustering structures depending on the selected Gaussian mixture model. The method aims at finding those directions which are able to display both variation in cluster means and variations in cluster covariances. The resulting MCLUSTSIR variables are defined as a linear mapping method which projects the data onto a suitable subspace.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11391/39200
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