  We provide a new framework for generating multiple good quality partitions
(clusterings) of a single data set. Our approach decomposes this problem into
two components, generating many high-quality partitions, and then grouping
these partitions to obtain k representatives. The decomposition makes the
approach extremely modular and allows us to optimize various criteria that
control the choice of representative partitions.
