Approximation algorithms for stochastic clustering

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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David Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas Pensyl


We consider stochastic settings for clustering, and develop provably-good (approximation) algorithms for a number of these notions. These algorithms allow one to obtain better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including providing fairer clustering and clustering which 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.