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Approximation algorithms for stochastic clustering*

2020-02-17 | |  36 |   32 |   0

Abstract 

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. KEYWORDS: clustering, k-center, k-median, lottery, approximation algorithms

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