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Minimum-entropy data partitioning using reversible jump Markov chain Monte Carlo

Abstract:

Problems in data analysis often require the unsupervised partitioning of a data set into classes. Several methods exist for such partitioning but many have the weakness of being formulated via strict parametric models (e.g., each class is modeled by a single Gaussian) or being computationally intensive in high-dimensional data spaces. We reconsider the notion of such cluster analysis in information-theoretic terms and show that an efficient partitioning may be given via a minimization of part...

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Publication status:
Published

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Publisher copy:
10.1109/34.946994

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume:
23
Issue:
8
Pages:
909-914
Publication date:
2001-08-05
DOI:
ISSN:
0162-8828
URN:
uuid:7c4188f0-8685-4b57-8f41-de4e0b8d768e
Source identifiers:
62949
Local pid:
pubs:62949

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