  The clustering problem, in its many variants, has numerous applications in
operations research and computer science (e.g., in applications in
bioinformatics, image processing, social network analysis, etc.). As sizes of
data sets have grown rapidly, researchers have focused on designing algorithms
for clustering problems in models of computation suited for large-scale
computation such as MapReduce, Pregel, and streaming models. The $k$-machine
model (Klauck et al., SODA 2015) is a simple, message-passing model for
large-scale distributed graph processing. This paper considers three of the
most prominent examples of clustering problems: the uncapacitated facility
location problem, the $p$-median problem, and the $p$-center problem and
presents $O(1)$-factor approximation algorithms for these problems running in
$\tilde{O}(n/k)$ rounds in the $k$-machine model. These algorithms are optimal
up to polylogarithmic factors because this paper also shows
$\tilde{\Omega}(n/k)$ lower bounds for obtaining polynomial-factor
approximation algorithms for these problems. These are the first results for
clustering problems in the $k$-machine model.
  We assume that the metric provided as input for these clustering problems in
only implicitly provided, as an edge-weighted graph and in a nutshell, our main
technical contribution is to show that constant-factor approximation algorithms
for all three clustering problems can be obtained by learning only a small
portion of the input metric.
