Clustering With Instance-Level Constraints

Abstract

We posit that problem-specific constraints can be incorporated into clustering algorithms to increase accuracy and decrease runtime. In experiments with a partitioning variant of COBWEB, we show marked improvements with surprisingly few constraints on three of four data sets. We also identify different types of constraints as appropriate in different settings.