Supervised Clustering with Support Vector Machines

Posted in Science on August 06, 2008


Supervised Clustering with Support Vector Machines

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.

Author: Thomas Finley, Cornell University

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Tags: Science, Lectures, Computer Science, Clustering, Machine Learning, VideoLectures.Net, Kernel Methods