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Integrating Constraints and Metric Learning in Semi-Supervised Clustering (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bilenko, Mikhail Basu, Sugato Mooney, Raymond J. |
| Description | 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 semisupervised clustering algorithms. In ICML |
| File Format | |
| Language | English |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Unified Approach Small Amount Previous Work Constraint-based Method Metric Learning Semi-supervised Clustering Underlying Similarity Unsupervised Learning Individual Approach New Semi-supervised Clustering Algorithm Experimental Result Clustering Algorithm New Method |
| Content Type | Text |
| Resource Type | Article |
