  We consider the {\em clustering with diversity} problem: given a set of
colored points in a metric space, partition them into clusters such that each
cluster has at least $\ell$ points, all of which have distinct colors.
  We give a 2-approximation to this problem for any $\ell$ when the objective
is to minimize the maximum radius of any cluster. We show that the
approximation ratio is optimal unless $\mathbf{P=NP}$, by providing a matching
lower bound. Several extensions to our algorithm have also been developed for
handling outliers. This problem is mainly motivated by applications in
privacy-preserving data publication.
