Supervised Clustering with Support Vector Machines
Abstract
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn how to cluster future sets of items. Example applications include noun-phrase coreference clustering, and clustering news articles by whether they refer to the same topic. In this paper we present an SVM algorithm that trains a clustering algorithm by adapting the item-pair similarity measure. The algorithm may optimize a variety of different clustering functions to a variety of clustering performance measures. We empirically evaluate the algorithm for noun-phrase and news article clustering.
Cite
Text
Finley and Joachims. "Supervised Clustering with Support Vector Machines." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102379Markdown
[Finley and Joachims. "Supervised Clustering with Support Vector Machines." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/finley2005icml-supervised/) doi:10.1145/1102351.1102379BibTeX
@inproceedings{finley2005icml-supervised,
title = {{Supervised Clustering with Support Vector Machines}},
author = {Finley, Thomas and Joachims, Thorsten},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {217-224},
doi = {10.1145/1102351.1102379},
url = {https://mlanthology.org/icml/2005/finley2005icml-supervised/}
}