Semi-supervised clustering uses a small amount of supervised
ta to aid unsupervised learning. One typical approach
ecifies a limited number of must-link and cannotlink
nstraints between pairs of examples. This paper
esents a pairwise constrained clustering framework and a
w method for actively selecting informative pairwise constraints
get improved clustering performance. The clustering
d active learning methods are both easily scalable
large datasets, and can handle very high dimensional data.
perimental and theoretical results confirm that this active
erying of pairwise constraints significantly improves the
curacy of clustering when given a relatively small amount
supervision.