Scalable Algorithms for Individual Preference Stable Clustering
<|reference_start|>Scalable Algorithms for Individual Preference Stable Clustering: In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is $\alpha$-IP stable when each data point's average distance to its cluster is no more than $\alpha$ times its average distance to any other cluster. In this paper, we study the natural local search algorithm for IP stable clustering. Our analysis confirms a $O(\log n)$-IP stability guarantee for this algorithm, where $n$ denotes the number of points in the input. Furthermore, by refining the local search approach, we show it runs in an almost linear time, $\tilde{O}(nk)$.<|reference_end|>
@article{mosenzon2024scalable,
    title={Scalable Algorithms for Individual Preference Stable Clustering},
    author={Ron Mosenzon, Ali Vakilian},
    journal={arXiv preprint arXiv:2403.10365},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10365},
    primaryClass={cs.DS cs.AI cs.CY cs.LG}
}