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Article Dans Une Revue Journal of Classification Année : 2020

Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering

Résumé

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.
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Dates et versions

hal-01949155 , version 1 (09-12-2018)
hal-01949155 , version 2 (10-12-2019)
hal-01949155 , version 3 (12-01-2021)

Identifiants

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Christophe Biernacki, Matthieu Marbac, Vincent Vandewalle. Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering. Journal of Classification, 2020, ⟨10.1007/s00357-020-09369-y⟩. ⟨hal-01949155v3⟩
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