Coupled Clustering: a Method for Detecting Structural Correspondence

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dc.creatorMarx, Zvika
dc.creatorDagan, Ido
dc.creatorBuhmann, Joachim
dc.date2001-07-23
dc.date.accessioned2026-07-07T03:17:23Z
dc.date.available2026-07-07T03:17:23Z
dc.descriptionThis paper proposes a new paradigm and computational framework for identification of correspondences between sub-structures of distinct composite systems. For this, we define and investigate a variant of traditional data clustering, termed coupled clustering, which simultaneously identifies corresponding clusters within two data sets. The presented method is demonstrated and evaluated for detecting topical correspondences in textual corpora.
dc.descriptionhtml with 5 figures
dc.identifierhttps://arxiv.org/abs/cs/0107032
dc.identifierhttp://arxiv.org/abs/cs/0107032
dc.identifierIn: C. E. Brodley and A. P. Danyluk (eds.), Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 353-360
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30702
dc.subjectMachine Learning
dc.subjectComputation and Language
dc.subjectInformation Retrieval
dc.subjectH.3.3; I.2.6; I.2.7; I.5.3; I.5.4
dc.titleCoupled Clustering: a Method for Detecting Structural Correspondence
dc.typetext

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