Integrating constraints and metric learning in semi-supervised clustering

Authors:
Mikhail Bilenko;Sugato Basu;Raymond J. Mooney
Affiliations:
University of Texas at Austin, Austin, TX;University of Texas at Austin, Austin, TX;University of Texas at Austin, Austin, TX
Venue:
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Year:
2004

Citing 9
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Abstract

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.