Coupled clustering: a method for detecting structural correspondence

Authors:
Zvika Marx;Ido Dagan;Joachim M. Buhmann;Eli Shamir
Affiliations:
The Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Givat-Ram, Jerusalem 91904, Israel and Department of Computer Science, Bar-Ilan University, Ramat-Gan 52900 ...;Department of Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel;Institut für Informatik III, University of Bonn, Römerstr. 164, D-53117 Bonn, Germany;School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat-Ram, Jerusalem 91904, Israel
Venue:
The Journal of Machine Learning Research
Year:
2003

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Abstract

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.