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Paper detail

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.

preprint2011arXivOpen access
Jeff M. PhillipsParasaran RamanSuresh Venkatasubramanian
DatabasesMachine Learning
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Jeff M. PhillipsParasaran RamanSuresh Venkatasubramanian

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