Clustering with Instance-Level Constraints

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 17

Student Abstracts

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.