This package is considered a duplicate. The official version of this package is found at:https://robertoalcantara9.r-universe.dev/LearnClust

Package: LearnClust 1.1

Roberto Alcantara

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.

Authors:Roberto Alcantara [aut, cre], Juan Jose Cuadrado [aut], Universidad de Alcala de Henares [aut]

LearnClust_1.1.tar.gz
LearnClust_1.1.zip(r-4.7-any)LearnClust_1.1.zip(r-4.6-any)LearnClust_1.1.zip(r-4.5-any)
LearnClust_1.1.tgz(r-4.6-any)LearnClust_1.1.tgz(r-4.5-any)
LearnClust_1.1.tar.gz(r-4.7-any)LearnClust_1.1.tar.gz(r-4.6-any)
LearnClust_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
LearnClust/json (API)

# Install 'LearnClust' in R:

install.packages('LearnClust', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

archived packages r-universe

2.85 score 5 stars 14 scripts 239 downloads 63 exports 4 dependencies

Last updated from:f13c9f5c1f (on package/LearnClust). Checks:7 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel	NOTE	171
source / vignettes	OK	159
linux-release	NOTE	115
macos-release	NOTE	104
macos-oldrel	NOTE	122
windows-devel	NOTE	76
windows-release	NOTE	82
windows-oldrel	NOTE	85
wasm-release	OK	115

Exports:agglomerativeHC agglomerativeHC.details canberradistance canberradistance.details canberradistanceW canberradistanceW.details chebyshevDistance chebyshevDistance.details chebyshevDistanceW chebyshevDistanceW.details clusterDistance clusterDistance.details clusterDistanceByApproach clusterDistanceByApproach.details complementaryClusters complementaryClusters.details correlationHC correlationHC.details distances distances.details divisiveHC divisiveHC.details edistance edistance.details edistanceW edistanceW.details getCluster getCluster.details getClusterDivisive getClusterDivisive.details initClusters initClusters.details initData initData.details initImages initTarget initTarget.details matrixDistance maxDistance maxDistance.details mdAgglomerative mdAgglomerative.details mdDivisive mdDivisive.details mdistance mdistance.details mdistanceW mdistanceW.details minDistance minDistance.details newCluster newCluster.details normalizeWeight normalizeWeight.details octileDistance octileDistance.details octileDistanceW octileDistanceW.details toList toList.details toListDivisive toListDivisive.details usefulClusters

Dependencies:curl magick magrittr Rcpp

Learning Clusterization

Rendered fromLearnClust.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2026-10-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
To execute agglomerative hierarchical clusterization algorithm by distance and approach.	agglomerativeHC
To explain agglomerative hierarchical clusterization algorithm by distance and approach.	agglomerativeHC.details
To calculate the Canberra distance.	canberradistance
To show the formula and to return the Canberra distance.	canberradistance.details
To calculate the Canberra distance applying weights.	canberradistanceW
To calculate the Canberra distance applying weights .	canberradistanceW.details
To calculate the Chebyshev distance.	chebyshevDistance
To show the formula of the Chebyshev distance.	chebyshevDistance.details
To calculate the Chebyshev distance applying weights.	chebyshevDistanceW
To calculate the Chebyshev distance applying weights.	chebyshevDistanceW.details
To calculate the distance between clusters.	clusterDistance
To explain how to calculate the distance between clusters.	clusterDistance.details
To calculate the distance by approach option.	clusterDistanceByApproach
To explain how to calculate the distance by approach option.	clusterDistanceByApproach.details
To check if two clusters are complementary	complementaryClusters
To explain how and why two clusters are complementary.	complementaryClusters.details
To execute hierarchical correlation algorithm.	correlationHC
To explain how hierarchical correlation algorithm works.	correlationHC.details
To calculate distances applying weights.	distances
To calculate distances applying weights.	distances.details
To execute divisive hierarchical clusterization algorithm by distance and approach.	divisiveHC
To explain the divisive hierarchical clusterization algorithm by distance and approach.	divisiveHC.details
To calculate the Euclidean distance.	edistance
To show the Euclidean distance formula.	edistance.details
To calculate the Euclidean distance applying weights.	edistanceW
To calculate the Euclidean distance applying weights.	edistanceW.details
To get the clusters with minimal distance.	getCluster
To explain how to get the clusters with minimal distance.	getCluster.details
To get the clusters with maximal distance.	getClusterDivisive
To explain how to get the clusters with maximal distance.	getClusterDivisive.details
To initialize clusters for the divisive algorithm.	initClusters
To explain how to initialize clusters for the divisive algorithm.	initClusters.details
To initialize data, hierarchical correlation algorithm.	initData
To initialize data, hierarchical correlation algorithm.	initData.details
To display an image.	initImages
To initialize target, hierarchical correlation algorithm.	initTarget
To initialize target, hierarchical correlation algorithm.	initTarget.details
Matrix distance by distance type	matrixDistance
Maximal distance	maxDistance
Maximal distance	maxDistance.details
Matrix distance by distance and approach type.	mdAgglomerative
Matrix distance by distance and approach type.	mdAgglomerative.details
Matrix distance by distance and approach type.	mdDivisive
Matrix distance by distance and approach type.	mdDivisive.details
To calculate the Manhattan distance.	mdistance
To explain how to calculate the Manhattan distance.	mdistance.details
To calculate the Manhattan distance applying weights.	mdistanceW
To calculate the Manhattan distance applying weights.	mdistanceW.details
Minimal distance	minDistance
Minimal distance	minDistance.details
To create a new cluster.	newCluster
To explain how to create a new cluster.	newCluster.details
To normalize weight values.	normalizeWeight
To normalize weight values.	normalizeWeight.details
To calculate the Octile distance.	octileDistance
To explain how to calculate the Octile distance.	octileDistance.details
To calculate the Octile distance applying weights.	octileDistanceW
To calculate the Octile distance applying weights.	octileDistanceW.details
To transform data into list	toList
To explain how to transform data into list	toList.details
To transform data into list	toListDivisive
To explain how to transform data into list	toListDivisive.details
To delete clusters grouped.	usefulClusters