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LearnClust (version 1.1)

Learning Hierarchical Clustering Algorithms

Description

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.

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Version

Install

install.packages('LearnClust')

Monthly Downloads

198

Version

1.1

License

Unlimited

Maintainer

Roberto Alcantara

Last Published

November 29th, 2020

Functions in LearnClust (1.1)

agglomerativeHC

To execute agglomerative hierarchical clusterization algorithm by distance and approach.
chebyshevDistanceW

To calculate the Chebyshev distance applying weights.
chebyshevDistanceW.details

To calculate the Chebyshev distance applying weights.
agglomerativeHC.details

To explain agglomerative hierarchical clusterization algorithm by distance and approach.
clusterDistanceByApproach

To calculate the distance by approach option.
complementaryClusters

To check if two clusters are complementary
canberradistance.details

To show the formula and to return the Canberra distance.
canberradistance

To calculate the Canberra distance.
clusterDistanceByApproach.details

To explain how to calculate the distance by approach option.
getClusterDivisive

To get the clusters with maximal distance.
edistance.details

To show the Euclidean distance formula.
edistance

To calculate the Euclidean distance.
getClusterDivisive.details

To explain how to get the clusters with maximal distance.
distances.details

To calculate distances applying weights.
edistanceW

To calculate the Euclidean distance applying weights.
distances

To calculate distances applying weights.
complementaryClusters.details

To explain how and why two clusters are complementary.
initData

To initialize data, hierarchical correlation algorithm.
initClusters

To initialize clusters for the divisive algorithm.
initData.details

To initialize data, hierarchical correlation algorithm.
mdDivisive

Matrix distance by distance and approach type.
chebyshevDistance

To calculate the Chebyshev distance.
chebyshevDistance.details

To show the formula of the Chebyshev distance.
divisiveHC

To execute divisive hierarchical clusterization algorithm by distance and approach.
initImages

To display an image.
divisiveHC.details

To explain the divisive hierarchical clusterization algorithm by distance and approach.
initClusters.details

To explain how to initialize clusters for the divisive algorithm.
minDistance

Minimal distance
initTarget

To initialize target, hierarchical correlation algorithm.
mdAgglomerative.details

Matrix distance by distance and approach type.
mdAgglomerative

Matrix distance by distance and approach type.
usefulClusters

To delete clusters grouped.
mdistance

To calculate the Manhattan distance.
initTarget.details

To initialize target, hierarchical correlation algorithm.
clusterDistance

To calculate the distance between clusters.
matrixDistance

Matrix distance by distance type
clusterDistance.details

To explain how to calculate the distance between clusters.
edistanceW.details

To calculate the Euclidean distance applying weights.
octileDistance.details

To explain how to calculate the Octile distance.
mdistance.details

To explain how to calculate the Manhattan distance.
octileDistance

To calculate the Octile distance.
mdDivisive.details

Matrix distance by distance and approach type.
maxDistance.details

Maximal distance
normalizeWeight.details

To normalize weight values.
normalizeWeight

To normalize weight values.
maxDistance

Maximal distance
minDistance.details

Minimal distance
octileDistanceW

To calculate the Octile distance applying weights.
newCluster

To create a new cluster.
toListDivisive

To transform data into list
toListDivisive.details

To explain how to transform data into list
octileDistanceW.details

To calculate the Octile distance applying weights.
newCluster.details

To explain how to create a new cluster.
getCluster

To get the clusters with minimal distance.
canberradistanceW

To calculate the Canberra distance applying weights.
correlationHC.details

To explain how hierarchical correlation algorithm works.
correlationHC

To execute hierarchical correlation algorithm.
canberradistanceW.details

To calculate the Canberra distance applying weights .
getCluster.details

To explain how to get the clusters with minimal distance.
mdistanceW

To calculate the Manhattan distance applying weights.
mdistanceW.details

To calculate the Manhattan distance applying weights.
toList

To transform data into list
toList.details

To explain how to transform data into list