Approximation algorithms for stochastic clustering
David G. Harris, Li, Shi, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh · arXiv (Cornell University) · 2018
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.