2026-05-05T19:46:46Zhttps://uvadoc.uva.es/oai/request

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

00925njm 22002777a 4500 dc Barrio Tellado, Eustasio del author Inouzhe Valdés, Hristo author Loubes, Jean-Michel author 2022 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. 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 Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering