Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering

Christophe Biernacki (), Matthieu Marbac () and Vincent Vandewalle
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Christophe Biernacki: Inria, Université de Lille, CNRS - Laboratoire de mathématiques Painlevé
Matthieu Marbac: Univ. Rennes, Ensai, CNRS, CREST - UMR 9194
Vincent Vandewalle: Inria, Université de Lille - ULR 2694 Equipe Metrics

Journal of Classification, 2021, vol. 38, issue 1, No 10, 129-157

Abstract: Abstract A generic method is introduced to visualize in a “Gaussian-like way,” and onto ℝ 2 $\mathbb {R}^{2}$ , results of Gaussian or non-Gaussian–based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared with the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional, and network) and is implemented on the r package ClusVis.

Keywords: Dimension reduction; Gaussian mixture; Factorial analysis; Linear discriminant analysis; Model-based clustering; Visualization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00357-020-09369-y

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