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