LearnClust
Learning Hierarchical Clustering Algorithms

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LearnClust: Learning Hierarchical Clustering Algorithms
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Man pages for LearnClust
Learning Hierarchical Clustering Algorithms

agglomerativeHCTo execute agglomerative hierarchical clusterization...
agglomerativeHC.detailsTo explain agglomerative hierarchical clusterization...
canberradistanceTo calculate the Canberra distance.
canberradistance.detailsTo show the formula and to return the Canberra distance.
canberradistanceWTo calculate the Canberra distance applying weights.
canberradistanceW.detailsTo calculate the Canberra distance applying weights .
chebyshevDistanceTo calculate the Chebyshev distance.
chebyshevDistance.detailsTo show the formula of the Chebyshev distance.
chebyshevDistanceWTo calculate the Chebyshev distance applying weights.
chebyshevDistanceW.detailsTo calculate the Chebyshev distance applying weights.
clusterDistanceTo calculate the distance between clusters.
clusterDistanceByApproachTo calculate the distance by approach option.
clusterDistanceByApproach.detailsTo explain how to calculate the distance by approach option.
clusterDistance.detailsTo explain how to calculate the distance between clusters.
complementaryClustersTo check if two clusters are complementary
complementaryClusters.detailsTo explain how and why two clusters are complementary.
correlationHCTo execute hierarchical correlation algorithm.
correlationHC.detailsTo explain how hierarchical correlation algorithm works.
distancesTo calculate distances applying weights.
distances.detailsTo calculate distances applying weights.
divisiveHCTo execute divisive hierarchical clusterization algorithm by...
divisiveHC.detailsTo explain the divisive hierarchical clusterization algorithm...
edistanceTo calculate the Euclidean distance.
edistance.detailsTo show the Euclidean distance formula.
edistanceWTo calculate the Euclidean distance applying weights.
edistanceW.detailsTo calculate the Euclidean distance applying weights.
getClusterTo get the clusters with minimal distance.
getCluster.detailsTo explain how to get the clusters with minimal distance.
getClusterDivisiveTo get the clusters with maximal distance.
getClusterDivisive.detailsTo explain how to get the clusters with maximal distance.
initClustersTo initialize clusters for the divisive algorithm.
initClusters.detailsTo explain how to initialize clusters for the divisive...
initDataTo initialize data, hierarchical correlation algorithm.
initData.detailsTo initialize data, hierarchical correlation algorithm.
initImagesTo display an image.
initTargetTo initialize target, hierarchical correlation algorithm.
initTarget.detailsTo initialize target, hierarchical correlation algorithm.
matrixDistanceMatrix distance by distance type
maxDistanceMaximal distance
maxDistance.detailsMaximal distance
mdAgglomerativeMatrix distance by distance and approach type.
mdAgglomerative.detailsMatrix distance by distance and approach type.
mdDivisiveMatrix distance by distance and approach type.
mdDivisive.detailsMatrix distance by distance and approach type.
mdistanceTo calculate the Manhattan distance.
mdistance.detailsTo explain how to calculate the Manhattan distance.
mdistanceWTo calculate the Manhattan distance applying weights.
mdistanceW.detailsTo calculate the Manhattan distance applying weights.
minDistanceMinimal distance
minDistance.detailsMinimal distance
newClusterTo create a new cluster.
newCluster.detailsTo explain how to create a new cluster.
normalizeWeightTo normalize weight values.
normalizeWeight.detailsTo normalize weight values.
octileDistanceTo calculate the Octile distance.
octileDistance.detailsTo explain how to calculate the Octile distance.
octileDistanceWTo calculate the Octile distance applying weights.
octileDistanceW.detailsTo calculate the Octile distance applying weights.
toListTo transform data into list
toList.detailsTo explain how to transform data into list
toListDivisiveTo transform data into list
toListDivisive.detailsTo explain how to transform data into list
usefulClustersTo delete clusters grouped.
LearnClust documentation built on Nov. 30, 2020, 1:09 a.m.

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