Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Abstract The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated a...

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Autores principales: Gregoire Preud’homme, Kevin Duarte, Kevin Dalleau, Claire Lacomblez, Emmanuel Bresso, Malika Smaïl-Tabbone, Miguel Couceiro, Marie-Dominique Devignes, Masatake Kobayashi, Olivier Huttin, João Pedro Ferreira, Faiez Zannad, Patrick Rossignol, Nicolas Girerd
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/26646d58a34f4588aac72200ad772731
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https://doaj.org/article/26646d58a34f4588aac72200ad772731

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