R: Learning Hierarchical Clustering Algorithms

# Learning Hierarchical Clustering Algorithms

## Documentation for package ‘LearnClust’ version 1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

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