A Lagrangian-based approach to learn distance metrics for clustering with minimal data transformation

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
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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