Supervised clustering with support vector machines

  • Authors:
  • Thomas Finley;Thorsten Joachims

  • Affiliations:
  • Cornell University, Ithaca, NY;Cornell University, Ithaca, NY

  • Venue:
  • ICML '05 Proceedings of the 22nd international conference on Machine learning
  • Year:
  • 2005

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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.