# Man pages for LearnClustLearning Hierarchical Clustering Algorithms

 agglomerativeHC To execute agglomerative hierarchical clusterization... agglomerativeHC.details To explain agglomerative hierarchical clusterization... 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. clusterDistanceByApproach To calculate the distance by approach option. clusterDistanceByApproach.details To explain how to calculate the distance by approach option. clusterDistance.details To explain how to calculate the distance between clusters. 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... divisiveHC.details To explain the divisive hierarchical clusterization algorithm... 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... 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.
LearnClust documentation built on Nov. 30, 2020, 1:09 a.m.