Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms are appropriate for a data set.
Version: | 0.1.1.0 |
Depends: | R (≥ 3.4.0) |
Imports: | diptest, splines |
Suggests: | testthat |
Published: | 2020-03-04 |
DOI: | 10.32614/CRAN.package.clusterability |
Author: | Zachariah Neville [aut, cre], Naomi Brownstein [aut], Maya Ackerman [aut], Andreas Adolfsson [aut] |
Maintainer: | Zachariah Neville <z.neville at stat.fsu.edu> |
License: | GPL-2 |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | clusterability results |
Reference manual: | clusterability.pdf |
Package source: | clusterability_0.1.1.0.tar.gz |
Windows binaries: | r-devel: clusterability_0.1.1.0.zip, r-release: clusterability_0.1.1.0.zip, r-oldrel: clusterability_0.1.1.0.zip |
macOS binaries: | r-release (arm64): clusterability_0.1.1.0.tgz, r-oldrel (arm64): clusterability_0.1.1.0.tgz, r-release (x86_64): clusterability_0.1.1.0.tgz, r-oldrel (x86_64): clusterability_0.1.1.0.tgz |
Old sources: | clusterability archive |
Reverse suggests: | FCPS |
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