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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.<\/jats:p>","DOI":"10.1007\/s11634-022-00516-4","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:05:16Z","timestamp":1666281916000},"page":"859-896","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering"],"prefix":"10.1007","volume":"17","author":[{"given":"Eustasio","family":"del Barrio","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3662-518X","authenticated-orcid":false,"given":"Hristo","family":"Inouzhe","sequence":"additional","affiliation":[]},{"given":"Jean-Michel","family":"Loubes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"516_CR1","doi-asserted-by":"crossref","unstructured":"Abbasi M, Bhaskara A, Venkatasubramanian S (2021) Fair clustering via equitable group representations. 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