Integrating Constraints and Metric Learning in Semi-Supervised Clustering
Abstract
Semi-supervised clustering employs a small amount of labeled data to aidunsupervised learning. Previous work in the area has utilized supervised datain one of two approaches: 1) constraint-based methods that guide theclustering algorithm towards a better grouping of the data, and 2)distance-function learning methods that adapt the underlying similarity metricused by the clustering algorithm. This paper provides new methods for the twoapproaches as well as presents a new semi-supervised clustering algorithm thatintegrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces betterclusters than both individual approaches as well as previously proposedsemi-supervised clustering algorithms.
Cite
Text
Bilenko et al. "Integrating Constraints and Metric Learning in Semi-Supervised Clustering." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015360Markdown
[Bilenko et al. "Integrating Constraints and Metric Learning in Semi-Supervised Clustering." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/bilenko2004icml-integrating/) doi:10.1145/1015330.1015360BibTeX
@inproceedings{bilenko2004icml-integrating,
title = {{Integrating Constraints and Metric Learning in Semi-Supervised Clustering}},
author = {Bilenko, Mikhail and Basu, Sugato and Mooney, Raymond J.},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015360},
url = {https://mlanthology.org/icml/2004/bilenko2004icml-integrating/}
}