2004 IntegratingConstraintsAndMetricLearning

• (Bilenko et al., 2004) ⇒ Mikhail Bilenko, Sugato Basu, Raymond Mooney. (2004). “Integrating Constraints and Metric Learning in Semi-Supervised Clustering.” In: Proceedings of the twenty-first International Conference on Machine learning. doi:10.1145/1015330.1015360

Subject Headings: Metric-based Learning, Semi-Supervised Clustering.

Cited By

• Cited by ~217 http://scholar.google.com/scholar?cites=17622499764936668164

Quotes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 IntegratingConstraintsAndMetricLearning	Sugato Basu Mikhail Bilenko Raymond J. Mooney			Integrating Constraints and Metric Learning in Semi-Supervised Clustering			http://www.cs.utexas.edu/~ml/papers/semi-icml-04.pdf	10.1145/1015330.1015360