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LearnClust: Learning Hierarchical Clustering Algorithms

Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step. It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. The package explains non expert users how hierarchical clustering algorithms work.

Version: 1.1
Depends: magick
Suggests: knitr, rmarkdown
Published: 2020-11-29
DOI: 10.32614/CRAN.package.LearnClust
Author: Roberto Alcantara [aut, cre], Juan Jose Cuadrado [aut], Universidad de Alcala de Henares [aut]
Maintainer: Roberto Alcantara <roberto.alcantara at edu.uah.es>
License: Unlimited
NeedsCompilation: no
CRAN checks: LearnClust results

Documentation:

Reference manual: LearnClust.pdf
Vignettes: Learning Clusterization

Downloads:

Package source: LearnClust_1.1.tar.gz
Windows binaries: r-devel: LearnClust_1.1.zip, r-release: LearnClust_1.1.zip, r-oldrel: LearnClust_1.1.zip
macOS binaries: r-release (arm64): LearnClust_1.1.tgz, r-oldrel (arm64): LearnClust_1.1.tgz, r-release (x86_64): LearnClust_1.1.tgz, r-oldrel (x86_64): LearnClust_1.1.tgz
Old sources: LearnClust archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=LearnClust to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.