New clustering methods for interval data (Q880906)
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scientific article; zbMATH DE number 5158597
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | New clustering methods for interval data |
scientific article; zbMATH DE number 5158597 |
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New clustering methods for interval data (English)
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29 May 2007
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Two new clustering methods are proposed for multivariate interval data. They are modifications of the dynamic \(k\)-clustering algorithm based on minimization of distances from elements of the cluster to its prototype (centre). The number \(k\) of clusters is fixed. A sum of Hausdorff distances between intervals is used in the first modification and a specific dissimilarity measure based on a discretization of the intervals is used in the second modification. These algorithms are applied to data on monthly minimal and maximal daily average temperatures in 60 meteorological stations in China.
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symbolic data
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multivariate interval data
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dynamic k-clustering
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Hausdorff distance
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