Euclidean k-center Fair Clusterings

TL;DR

This paper introduces a new variant of fair clustering on the plane, aiming to partition points into clusters that satisfy color-based size constraints while minimizing total clustering cost.

Contribution

It defines a natural fair clustering problem with color bounds and provides a polynomial time algorithm for computing an optimal solution.

Findings

The algorithm guarantees an optimal fair clustering respecting color bounds.

The problem is formulated as a Euclidean k-center clustering with fairness constraints.

The approach extends existing clustering methods to incorporate fairness in a polynomial time framework.

Abstract

Many approximation algorithms and heuristic algorithms to find a fair clustering have emerged. In this paper we define a new and natural variant of fair clustering problem and design a polynomial time algorithm to compute an optimal fair clustering. Let P be a set of n points on a plane, and each point has a color in C, corresponding to a group. For each color q in C, a lower bound l(q) and an upper bound u(q) are given. Then we define the fair clustering problem as follows. The fair k-clustering problem is to find a partition of P into a set of k clusters with a minimum cost such that each cluster contains at least l(q) and at most u(q) points in P with color q. By l(q) and u(q) each cluster cannot contain too few or too many points with a specific color. If we regard a color to a gender or a minority ethnic group, the clustering corresponds to a fair clustering.