Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.
In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 81-88, Banff, Canada, July 2004.

Sugato Basu Ph.D. Alumni sugato [at] cs utexas edu
Mikhail Bilenko Ph.D. Alumni mbilenko [at] microsoft com
Raymond J. Mooney Faculty mooney [at] cs utexas edu