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arXiv · 1809.02271

Approximation algorithms for stochastic clustering

David G. Harris·Shi Li·Thomas Pensyl·Aravind Srinivasan·Khoa Trinh
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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.

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David G. Harris, Shi Li, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh. 2020-12-16. Approximation algorithms for stochastic clustering. https://arxiv.org/abs/1809.02271

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