Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering

Eustasio Barrio (), Hristo Inouzhe () and Jean-Michel Loubes ()
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Eustasio Barrio: Universidad de Valladolid
Hristo Inouzhe: Universidad de Valladolid
Jean-Michel Loubes: Université de Toulouse, Institut de Mathématiques de Toulouse

Advances in Data Analysis and Classification, 2023, vol. 17, issue 4, No 3, 859-896

Abstract: Abstract 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 protected 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.

Keywords: Diversity enhancing clustering; Demographic parity; Fair clustering; Hierarchical clustering; Kernel methods; Multidimensional scaling; MSC 62H30; MSC 68T10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11634-022-00516-4

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