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.File in questo prodotto:
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