Poster
Approximation algorithms for stochastic clustering
David Harris · Shi Li · Aravind Srinivasan · Khoa Trinh · Thomas Pensyl
Room 517 AB #134
Keywords: [ Combinatorial Optimization ] [ Fairness, Accountability, and Transparency ] [ Computational Complexity ]
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.
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