Coupled clustering: a method for detecting structural correspondence

Z Marx, I Dagan, J. M Buhmann, E Shamir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper proposes a new paradigm and a computational framework for revealing equivalencies (analogies) between sub-structures of distinct composite systems that are initially represented by unstructured data sets. For this purpose, we introduce and investigate a variant of traditional data clustering, termed coupled clustering, which outputs a configuration of corresponding subsets of two such representative sets. We apply our method to synthetic as well as textual data. Its achievements in detecting topical correspondences between textual corpora are evaluated through comparison to performance of human experts.
Original languageAmerican English
Title of host publication18th International Conference on Machine Learning (ICML)
StatePublished - 2001

Bibliographical note

Place of conference:Williamstown, Massachusetts , USA


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