  In discrete k-center and k-median clustering, we are given a set of points P
in a metric space M, and the task is to output a set C \subseteq ? P, |C| = k,
such that the cost of clustering P using C is as small as possible. For
k-center, the cost is the furthest a point has to travel to its nearest center,
whereas for k-median, the cost is the sum of all point to nearest center
distances. In the fault-tolerant versions of these problems, we are given an
additional parameter 1 ?\leq \ell \leq ? k, such that when computing the cost
of clustering, points are assigned to their \ell-th nearest-neighbor in C,
instead of their nearest neighbor. We provide constant factor approximation
algorithms for these problems that are both conceptually simple and highly
practical from an implementation stand-point.
