constructs a cluster hierarchy based on the distance or dissimilarity matrix m.
associates the elements of list with the rows of the matrix m in the cluster hierarchy.
- To use , you first need to load the Hierarchical Clustering Package using Needs["HierarchicalClustering`"].
- DirectAgglomerate[m] labels each element by its row position in the matrix m.
- gives a Cluster object.
- The cluster hierarchy may be viewed using DendrogramPlot.
- The distance matrix m can be any symmetric matrix.
- The method used to determine intercluster dissimilarity can be specified using the Linkage option.
- Possible settings for the Linkage option include:
"Single" smallest intercluster dissimilarity "Average" average intercluster dissimilarity "Complete" largest intercluster dissimilarity "WeightedAverage" weighted average intercluster dissimilarity "Centroid" distance from cluster centroids "Median" distance from cluster medians "Ward" Ward's minimum variance dissimilarity f a pure function
- The function f defines a distance from a cluster k to the new cluster formed by fusing clusters i and j.
- The arguments supplied to f are , , , , , and , where d is the distance between clusters and n is the number of elements in a cluster.