Source: r-cran-genieclust
Section: gnu-r
Priority: optional
Maintainer: Debian R Packages Maintainers
Uploaders: Andreas Tille
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-genieclust
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-genieclust.git
Homepage: https://cran.r-project.org/package=genieclust
Standards-Version: 4.6.2
Rules-Requires-Root: no
Build-Depends: debhelper-compat (= 13),
dh-r,
r-base-dev,
r-cran-rcpp (>= 1.0.4)
Testsuite: autopkgtest-pkg-r
Package: r-cran-genieclust
Architecture: any
Depends: ${R:Depends},
${shlibs:Depends},
${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: GNU R Genie++ Hierarchical Clustering Algorithm with Noise Points Detection
A retake on the Genie algorithm - a robust hierarchical clustering
method (Gagolewski, Bartoszuk, Cena, 2016
). Now faster and more memory efficient;
determining the whole hierarchy for datasets of 10M points in low
dimensional Euclidean spaces or 100K points in high-dimensional ones
takes only 1-2 minutes. Allows clustering with respect to mutual
reachability distances so that it can act as a noise point detector or a
robustified version of 'HDBSCAN*' (that is able to detect a predefined
number of clusters and hence it does not dependent on the somewhat
fragile 'eps' parameter).
.
The package also features an implementation of economic inequity indices
(the Gini, Bonferroni index) and external cluster validity measures
(partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows,
adjusted mutual information, pair sets index).
.
See also the 'Python' version of 'genieclust' available on 'PyPI', which
supports sparse data, more metrics, and even larger datasets.