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Improving Constrained Clustering with Active Query

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.
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Submitted on : Tuesday, July 7, 2015 - 4:01:22 PM
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Viet Vu Vu, Nicolas Labroche, Bernadette Bouchon-Meunier. Improving Constrained Clustering with Active Query. Pattern Recognition, Elsevier, 2012, 45 (4), pp.1749-1758. ⟨10.1016/j.patcog.2011.10.016⟩. ⟨hal-01172643⟩



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