29 lines
1.2 KiB
XML
29 lines
1.2 KiB
XML
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<?xml version='1.0' encoding='UTF-8'?>
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<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
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<pkgmetadata>
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<maintainer type="person">
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<email>gentoo@chymera.eu</email>
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<name>Horea Christian</name>
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</maintainer>
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<maintainer type="project">
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<email>sci@gentoo.org</email>
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<name>Gentoo Science Project</name>
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</maintainer>
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<longdescription lang="en">
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HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
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Noise. Performs DBSCAN over varying epsilon values and integrates the result
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to find a clustering that gives the best stability over epsilon. This allows
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HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more
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robust to parameter selection.
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In practice this means that HDBSCAN returns a good clustering straight away
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with little or no parameter tuning -- and the primary parameter, minimum
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cluster size, is intuitive and easy to select. HDBSCAN is ideal for
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exploratory data analysis; it's a fast and robust algorithm that you can
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trust to return meaningful clusters (if there are any).
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</longdescription>
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<upstream>
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<remote-id type="github">scikit-learn-contrib/hdbscan</remote-id>
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</upstream>
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</pkgmetadata>
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