FPT Approximations for Fair $k$-Min-Sum-Radii

TL;DR

This paper develops fixed-parameter tractable approximation algorithms for the fair $k$-min-sum-radii clustering problem, achieving a $(6+ ext{epsilon})$-approximation generally and a $(3+ ext{epsilon})$-approximation for the 1:1 fairness case, advancing fair clustering methods.

Contribution

The paper introduces new FPT approximation algorithms for fair $k$-min-sum-radii clustering, improving approximation ratios for both general and specific fairness cases.

Findings

Achieves a $(6+ ext{epsilon})$-approximation for general fair $k$-MSR.

Improves to a $(3+ ext{epsilon})$-approximation for the 1:1 fairness case.

Extends FPT algorithms to incorporate fairness constraints in clustering.

Abstract

We consider the $k$-min-sum-radii ($k$-MSR) clustering problem with fairness constraints. The $k$-min-sum-radii problem is a mixture of the classical $k$-center and $k$-median problems. We are given a set of points $P$ in a metric space and a number $k$ and aim to partition the points into $k$ clusters, each of the clusters having one designated center. The objective to minimize is the sum of the radii of the $k$ clusters (where in $k$-center we would only consider the maximum radius and in $k$-median we would consider the sum of the individual points' costs). Various notions of fair clustering have been introduced lately, and we follow the definitions due to Chierichetti, Kumar, Lattanzi and Vassilvitskii [NeurIPS 2017] which demand that cluster compositions shall follow the proportions of the input point set with respect to some given sensitive attribute. For the easier case where the sensitive attribute only has two possible values and each is equally frequent in the input, the aim is to compute a clustering where all clusters have a 1:1 ratio with respect to this attribute. We call this the 1:1 case. There has been a surge of FPT-approximation algorithms for the $k$-MSR problem lately, solving the problem both in the unconstrained case and in several constrained problem variants. We add to this research area by designing an FPT $(6+\epsilon)$-approximation that works for $k$-MSR under the mentioned general fairness notion. For the special 1:1 case, we improve our algorithm to achieve a $(3+\epsilon)$-approximation.