  In this paper, we present distributed generalized clustering algorithms that
can handle large scale data across multiple machines in spite of straggling or
unreliable machines. We propose a novel data assignment scheme that enables us
to obtain global information about the entire data even when some machines fail
to respond with the results of the assigned local computations. The assignment
scheme leads to distributed algorithms with good approximation guarantees for a
variety of clustering and dimensionality reduction problems.
