  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.
