# Fair Center Clustering in Sliding Windows

arXiv: https://arxiv.org/abs/2503.05260 · HTML: https://mlsanity.com/paper/2503.05260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Streaming algorithms for robust max-min diversification](https://mlsanity.com/paper/2610.01456/) (2026)

## Abstract (arXiv)

The k-center problem requires the selection of k points (centers) from a given metric pointset W so to minimize the maximum distance of any point of W from the closest center. This paper focuses on a fair variant of the problem, known as emph fair center, where each input point belongs to some category and each category may contribute a limited number of points to the center set. We present the first space-efficient streaming algorithm for fair center in general metrics, under the sliding window model. At any time t, the algorithm is able to provide a solution for the current window whose quality is almost as good as the one guaranteed by the best, polynomial-time sequential algorithms run on the entire window, and exhibits space and time requirements independent of the window size. Our theoretical results are backed by an extensive set of experiments on both real-world and synthetic datasets, which provide evidence of the practical viability of the algorithm.