Title: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
Authors: Matthieu Marbac-Lourdelle - ENSAI and CREST (France)
Vincent Vandewalle - Inria (France)
Christophe Biernacki - Inria (France) [presenting]
Abstract: A generic method is introduced to visualize in a Gaussian-like way, and onto $R^d$, results of Gaussian or non-Gaussian model-based clustering. The key point is to explicitly force a spherical Gaussian mixture visualization to inherit from the within cluster overlap which is present in the initial clustering mixture. The result is a particularly user-friendly draw of the clusters, allowing any practitioner to have an overview of the potentially complex clustering result. An entropic measure allows us to inform of the quality of the drawn overlap, in comparison 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.