Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering

Christophe Biernacki (1) , Matthieu Marbac (2) , Vincent Vandewalle (3)

1 LPP - Laboratoire Paul Painlevé - UMR 8524
2 ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz]
3 MODAL - MOdel for Data Analysis and Learning

A generic method is introduced to visualize in a "Gaussian-like way", and onto 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 to 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.

Mots clés: Gaussian mixture; factorial analysis; linear discriminant analysis; model-based clustering; visualization; Dimension reduction

Domaines: Méthodologie [stat.ME]

Christophe Biernacki, Matthieu Marbac, Vincent Vandewalle. Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering. 2019. ⟨hal-01949155v2⟩