Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has employed one of two approaches: 1) Search-based methods that utilize supervised data to guide the search for the best clustering, and 2) Similarity-based methods that use supervised data to adapt the underlying similarity metric used by the clustering algorithm. This paper presents a unified approach based on the K-Means clustering algorithm that incorporates both of these techniques. Experimental results demonstrate that the combined approach generally produces better clusters than either of the individual approaches.

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In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 42-49, Washington, DC, 2003.

@inproceedings{basu:ml03-wkshp, title={Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering}, author={Sugato Basu and Mikhail Bilenko and Raymond J. Mooney}, booktitle={Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining}, address={Washington, DC}, pages={42-49}, url="http://www.cs.utexas.edu/users/ai-lab/?basu:ml03-wkshp", year={2003} }