Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means
We study discrete k-clustering problems in general metric spaces that are constrained by a combination of two different fairness conditions within the demographic fairness model. Given a metric space (P,d), where every point in P is equipped with a protected attribute, and a number k, the goal is to partition P into k clusters with a designated center each, such that a center-based objective function is minimized and the attributes are fairly distributed with respect to the following two fairness concepts: 1) group fairness: We aim for clusters with balanced numbers of attributes by specifying lower and upper bounds for the desired attribute proportions. 2) diverse center selection: Clust...
| Authors | Nicole Funk, Annika Hennes, Johanna Hillebrand et al. |
|---|---|
| Primary category | cs.DS |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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