Online Active Constraint Selection For Semi-Supervised Clustering

Due to strong demand for the ability to enforce top-down structure on clustering results, semi-supervised clustering methods using pairwise constraints as side information have received increasing attention in recent years. However, most current methods are passive in the sense that the side information is provided beforehand and selected randomly. This may lead to the use of constraints that are redundant, unnecessary, or even harmful to the clustering results. To overcome this, we present an active clustering framework which selects pairwise constraints online as clustering proceeds, and propose an online constraint selection method that actively selects pairwise constraints by identifying uncertain nodes in the data. We also propose two novel methods for computing node uncertainty: one global and parametric and the other one local and nonparametric. We evaluate our active constraint selection method with two different semisupervised clustering algorithms on UCI, digits, gene and image datasets, and achieve results superior to current state of the art active techniques.