Approximation algorithms for stochastic clustering
Data Structures and Algorithms
2023-10-13 v5
Abstract
We consider stochastic settings for clustering, and develop provably-good approximation algorithms for a number of these notions. These algorithms yield better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including clustering which is fairer and has better long-term behavior for each user. In particular, they ensure that *every user* is guaranteed to get good service (on average). We also complement some of these with impossibility results.
Cite
@article{arxiv.1809.02271,
title = {Approximation algorithms for stochastic clustering},
author = {David G. Harris and Shi Li and Thomas Pensyl and Aravind Srinivasan and Khoa Trinh},
journal= {arXiv preprint arXiv:1809.02271},
year = {2023}
}
Comments
The version of this paper published in JMLR is incorrect; specifically, Theorem 14 of that paper appears to be fatally flawed. The version posted here on arxiv removes the claimed incorrect results