Package: LearnClust 1.1

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-4.5)LearnClust_1.1.zip(r-4.4)LearnClust_1.1.zip(r-4.3)
LearnClust_1.1.tgz(r-4.4-any)LearnClust_1.1.tgz(r-4.3-any)
LearnClust_1.1.tar.gz(r-4.5-noble)LearnClust_1.1.tar.gz(r-4.4-noble)
LearnClust_1.1.tgz(r-4.4-emscripten)LearnClust_1.1.tgz(r-4.3-emscripten)
LearnClust.pdf |LearnClust.html✨
LearnClust/json (API)

# Install 'LearnClust' in R:
install.packages('LearnClust', repos = c('https://robertoalcantara9.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

63 exports 0.00 score 4 dependencies 8 scripts 162 downloads

Last updated 4 years agofrom:10cd7f8a8a. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 22 2024
R-4.5-winNOTEAug 22 2024
R-4.5-linuxNOTEAug 22 2024
R-4.4-winNOTEAug 22 2024
R-4.4-macNOTEAug 22 2024
R-4.3-winOKAug 22 2024
R-4.3-macOKAug 22 2024

Exports:agglomerativeHCagglomerativeHC.detailscanberradistancecanberradistance.detailscanberradistanceWcanberradistanceW.detailschebyshevDistancechebyshevDistance.detailschebyshevDistanceWchebyshevDistanceW.detailsclusterDistanceclusterDistance.detailsclusterDistanceByApproachclusterDistanceByApproach.detailscomplementaryClusterscomplementaryClusters.detailscorrelationHCcorrelationHC.detailsdistancesdistances.detailsdivisiveHCdivisiveHC.detailsedistanceedistance.detailsedistanceWedistanceW.detailsgetClustergetCluster.detailsgetClusterDivisivegetClusterDivisive.detailsinitClustersinitClusters.detailsinitDatainitData.detailsinitImagesinitTargetinitTarget.detailsmatrixDistancemaxDistancemaxDistance.detailsmdAgglomerativemdAgglomerative.detailsmdDivisivemdDivisive.detailsmdistancemdistance.detailsmdistanceWmdistanceW.detailsminDistanceminDistance.detailsnewClusternewCluster.detailsnormalizeWeightnormalizeWeight.detailsoctileDistanceoctileDistance.detailsoctileDistanceWoctileDistanceW.detailstoListtoList.detailstoListDivisivetoListDivisive.detailsusefulClusters

Dependencies:curlmagickmagrittrRcpp

Learning Clusterization

Rendered fromLearnClust.Rmdusingknitr::rmarkdownon Aug 22 2024.

Last update: 2020-09-30
Started: 2020-09-30

Readme and manuals

Help Manual

Help pageTopics
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 complementarycomplementaryClusters
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 typematrixDistance
Maximal distancemaxDistance
Maximal distancemaxDistance.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 distanceminDistance
Minimal distanceminDistance.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 listtoList
To explain how to transform data into listtoList.details
To transform data into listtoListDivisive
To explain how to transform data into listtoListDivisive.details
To delete clusters grouped.usefulClusters