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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...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| 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 |