Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results |
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Authors: | Ian Davidson S S Ravi |
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Affiliation: | (1) Department of Computer Science, The University of California - Davis, Davis, CA 95616, USA;(2) Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222, USA |
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Abstract: | Clustering with constraints is a powerful method that allows users to specify background knowledge and the expected cluster
properties. Significant work has explored the incorporation of instance-level constraints into non-hierarchical clustering
but not into hierarchical clustering algorithms. In this paper we present a formal complexity analysis of the problem and
show that constraints can be used to not only improve the quality of the resultant dendrogram but also the efficiency of the
algorithms. This is particularly important since many agglomerative style algorithms have running times that are quadratic
(or faster growing) functions of the number of instances to be clustered. We present several bounds on the improvement in
the running times of algorithms obtainable using constraints.
A preliminary version of this paper appeared as Davidson and Ravi (2005b). |
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Keywords: | Clustering Constrained clustering Semi-supervised learning |
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