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.

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In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 81-88, Banff, Canada, July 2004.

@inproceedings{bilenko:ml04, title={Integrating Constraints and Metric Learning in Semi-Supervised Clustering}, author={Mikhail Bilenko and Sugato Basu and Raymond J. Mooney}, booktitle={Proceedings of 21st International Conference on Machine Learning (ICML-2004)}, month={July}, address={Banff, Canada}, pages={81-88}, url="http://www.cs.utexas.edu/users/ai-lab?bilenko:ml04", year={2004} }