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.
