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

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 and real-l...

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Detalles Bibliográficos
Autores principales: Preud’homme, Gregoire, Duarte, Kevin, Dalleau, Kevin, Lacomblez, Claire, Bresso, Emmanuel, Smaïl-Tabbone, Malika, Couceiro, Miguel, Devignes, Marie-Dominique, Kobayashi, Masatake, Huttin, Olivier, Ferreira, João Pedro, Zannad, Faiez, Rossignol, Patrick, Girerd, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892576/
https://www.ncbi.nlm.nih.gov/pubmed/33603019
http://dx.doi.org/10.1038/s41598-021-83340-8