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Multidimensional clustering and hypergraphs.

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Title: Multidimensional clustering and hypergraphs.
Authors: Kozyrev, S. V.1 kozyrev@mi.ras.ru
Source: Theoretical & Mathematical Physics. Sep2010, Vol. 164 Issue 3, p1163-1168. 6p.
Subjects: Hypergraphs, Fuzzy hypergraphs, Graph theory, Metric spaces, Quasi-metric spaces
Abstract: We discuss a multidimensional generalization of the clustering method. In our approach, the clustering is realized by partially ordered hypergraphs belonging to some family. The suggested procedure is applicable in the case where the original metric depends on a set of parameters. The clustering hypergraph studied here can be regarded as an object describing all possible clustering trees corresponding to different values of the original metric. [ABSTRACT FROM AUTHOR]
Copyright of Theoretical & Mathematical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We discuss a multidimensional generalization of the clustering method. In our approach, the clustering is realized by partially ordered hypergraphs belonging to some family. The suggested procedure is applicable in the case where the original metric depends on a set of parameters. The clustering hypergraph studied here can be regarded as an object describing all possible clustering trees corresponding to different values of the original metric. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Theoretical & Mathematical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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