Generating a Diverse Set of High-Quality Clusterings
  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. 
Jeff M. Phillips, Parasaran Raman, and Suresh Venkatasubramanian
cs.LG cs.DB