TY - JOUR
T1 - Coupled clustering: A method for detecting structural correspondence
T2 - A method for detecting structural correspondence
AU - Marx, Zvika
AU - Dagan, Ido
AU - Buhmann, Joachim M.
AU - Shamir, Eli
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2003/5/15
Y1 - 2003/5/15
N2 - 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.
AB - 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.
KW - Clustering
KW - Data mining in texts
KW - Natural language processing
KW - Structure mapping
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=141607829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/record.url?scp=0141607829&partnerID=8YFLogxK
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SN - 1532-4435
VL - 3
SP - 747
EP - 780
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
IS - 4-5
ER -