2026-04-27T12:35:38Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/570402022-11-14T20:00:46Zcom_10324_32197com_10324_952com_10324_894col_10324_32199

Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering Barrio Tellado, Eustasio del Inouzhe Valdés, Hristo Loubes, Jean-Michel Producción Científica We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of pro- tected attributes such as race, sex, age, etc. In the context of fair clustering, diversity plays a major role when fairness is understood as demographic parity. To promote diversity, we introduce perturbations to the distance in the unprotected attributes that account for protected attributes in a way that resembles attraction-repulsion of charged particles in Physics. These perturbations are defined through dissimilarities with a tractable interpretation. Cluster analysis based on attraction-repulsion dissimilarities penalizes homogeneity of the clusters with respect to the protected attributes and leads to an improvement in diversity. An advantage of our approach, which falls into a pre- processing set-up, is its compatibility with a wide variety of clustering methods and whit non-Euclidean data. We illustrate the use of our procedures with both synthetic and real data and provide discussion about the relation between diversity, fairness, and cluster structure. 2022-11-14T11:47:16Z 2022-11-14T11:47:16Z 2022 info:eu-repo/semantics/article Advances in Data Analysis and Classification, 2022, 1862-5347 https://uvadoc.uva.es/handle/10324/57040 10.1007/s11634-022-00516-4 Advances in Data Analysis and Classification 1862-5355 eng https://link.springer.com/article/10.1007/s11634-022-00516-4 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2022 The Author(s) Atribución 4.0 Internacional Springer SI