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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-83340-8 |