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Description:

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).

Homepage:

https://github.com/scikit-learn-contrib/hdbscan

License:

BSD

Versions

Metadata

Description

• 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).

Maintainers

• Horea Christian (gentoo@chymera.eu) - Type: person
• Gentoo Science Project (sci@gentoo.org) - Type: project

Upstream

• Remote IDs:
  • github: scikit-learn-contrib/hdbscan
  • pypi: hdbscan

<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>

Lint Warnings

• [Warning] Missing md5-cache for version 0.8.41. Run 'ebuild <ebuild_file> manifest' or 'egencache' to generate it.

Manifest