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Visualization of Model-Based Clustering Structures

  • Luca ScruccaEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Gaussian Mixture Model Kernel Matrix Finite Mixture Uncertainty Boundary Slice Inverse Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Economia, Finanza e StatisticaUniversità degli Studi di PerugiaPerugiaItaly

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