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


Reference manual: LearnClust.pdf
Vignettes: Learning Clusterization


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


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