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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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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.