Abstract
The incorporation of background knowledge in unsupervised algorithms has been shown to yield performance improvements in terms of model quality and execution speed. However, performance is dependent on the quantity and quality of the background knowledge being exploited. In this work, we study the issue of selecting Must-Link and Cannot-Link constraints for semi-supervised clustering. We propose “
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