Generating a Diverse Set of High-Quality Clusterings
arXiv:1108.0017
Abstract
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.
12 Pages, 5 Figures, 2nd MultiClust Workshop at ECML PKDD 2011