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2002 JMLR JMLR 2002

Coupled Clustering: A Method for Detecting Structural Correspondence

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. [abs] [pdf] [ps.gz] [ps]

📈 Trend Setter — Clustering
🧭 Keyword Pioneer — data clustering
🐣 Hot Topic Early Bird — unsupervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning

Keywords

unsupervised learning data clustering structural correspondence textual corpus analogy detection coupled clustering
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