Coupled clustering: A method for detecting structural correspondence

Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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.

Original languageEnglish
Pages (from-to)747-780
Number of pages34
JournalJournal of Machine Learning Research
Issue number4-5
StatePublished - 15 May 2003

Bibliographical note

Funding Information:
We acknowledge the support of the Hungarian state and the European Union TAMOP-4.2.2A-11/1/KONV-2012-0072 and TAMOP-4.1.1C-12/1/KONV-2012-0017. This paper was also supported by the J?nos Bolyai Research Scholarship of the Hungarian Academy of Sciences.


  • Clustering
  • Data mining in texts
  • Natural language processing
  • Structure mapping
  • Unsupervised learning


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