Improving constrained clustering with active query selection

Authors:
Viet-Vu Vu;Nicolas Labroche;Bernadette Bouchon-Meunier
Affiliations:
UPMC Univ Paris 06, UMR 7606, LIP6, F-75005 Paris, France;UPMC Univ Paris 06, UMR 7606, LIP6, F-75005 Paris, France;CNRS, UMR 7606, LIP6, F-75005 Paris, France
Venue:
Pattern Recognition
Year:
2012

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Abstract

In this article, we address the problem of automatic constraint selection to improve the performance of constraint-based clustering algorithms. To this aim we propose a novel active learning algorithm that relies on a k-nearest neighbors graph and a new constraint utility function to generate queries to the human expert. This mechanism is paired with propagation and refinement processes that limit the number of constraint candidates and introduce a minimal diversity in the proposed constraints. Existing constraint selection heuristics are based on a random selection or on a min-max criterion and thus are either inefficient or more adapted to spherical clusters. Contrary to these approaches, our method is designed to be beneficial for all constraint-based clustering algorithms. Comparative experiments conducted on real datasets and with two distinct representative constraint-based clustering algorithms show that our approach significantly improves clustering quality while minimizing the number of human expert solicitations.