Rodrigo Randel, Daniel Aloise
and Alain Hertz
Paper (2023)
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| Department: |
Department of Computer Engineering and Software Engineering Department of Mathematics and Industrial Engineering |
|---|---|
| ISBN: | 9781611977653 |
| PolyPublie URL: | https://publications.polymtl.ca/57230/ |
| Conference Title: | SIAM International Conference on Data Mining (SDM 2023) |
| Conference Location: | Minneapolis, MN, USA |
| Conference Date(s): | 2023-04-27 - 2023-04-29 |
| Publisher: | Society for Industrial and Applied Mathematics Publications |
| DOI: | 10.1137/1.9781611977653 |
| Official URL: | https://doi.org/10.1137/1.9781611977653 |
| Date Deposited: | 29 Jan 2024 14:38 |
| Last Modified: | 25 Sep 2024 16:49 |
| Cite in APA 7: | Randel, R., Aloise, D., & Hertz, A. (2023, April). A Lagrangian-based approach to learn distance metrics for clustering with minimal data transformation [Paper]. SIAM International Conference on Data Mining (SDM 2023), Minneapolis, MN, USA. https://doi.org/10.1137/1.9781611977653 |
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