| A nonparametric algorithm for detecting clusters using hierarchical structure. | |
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MedLine Citation:
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PMID: 21868905 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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The present paper discusses a nonparametric algorithm for detecting clusters. In the algorithm a positive value called potential is associated with each datum based on dissimilarities. By defining subordination relations among data, hierarchical structure is introduced into the data set. As a result of the introduction of hierarchical structure, the data set is divided into some subsets called subclusters. A procedure for constructing clusters from the subclusters is also considered. The proposed algorithm can be applied to a very wide range of data set and has great ability to detect clusters, which is verified by computer simulation. |
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Authors:
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R Mizoguchi; M Shimura |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: 2 ISSN: 0162-8828 ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Publication Date: 1980 Apr |
Date Detail:
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Created Date: 2011-08-26 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 292-300 Citation Subset: - |
Affiliation:
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MEMBER, IEEE, Institute of Scientific and Industrial Research, Osaka University, Suita, Osaka, Japan. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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