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 cannotlink
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.
