Proceedings of the SIAM International Conference on Data Mining (SDM-2004), pp. 333-344, Lake Buena Vista, FL, April 2004.
Paper ID: 141
Category: Active Learning, Text Categorization and Clustering, Unsupervised and Semi-Supervised Learning and Clustering
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannot-link constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision.
