Agglomerate

Agglomerate[{e1,e2,}]
gives a hierarchical clustering of the elements , , .

Agglomerate[{e1->v1,e2->v2,}]
represents with in each cluster.

Agglomerate[{e1,e2,}->{v1,v2,}]
represents with in each cluster.

DetailsDetails

  • To use Agglomerate, you first need to load the Hierarchical Clustering Package using Needs["HierarchicalClustering`"].
  • Agglomerate gives a Cluster object.
  • The cluster hierarchy may be viewed using DendrogramPlot.
  • The data elements can be numbers; numeric lists, matrices, or tensors; lists of Boolean elements; or strings. If the are lists, matrices, or tensors, each must have the same dimensions.
  • The following options can be given:
  • DistanceFunctionAutomaticthe distance or dissimilarity measure to use
    LinkageAutomaticthe clustering linkage algorithm to use
  • With the default setting DistanceFunction->Automatic, Agglomerate uses SquaredEuclideanDistance for numeric data, JaccardDissimilarity for Boolean data, and EditDistance for string data.
  • The setting for DistanceFunction can be any distance or dissimilarity function or a pure function f defining a distance between two values.
  • Linkage defines the intercluster dissimilarity, given the dissimilarities between member elements.
  • 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
    fa 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.
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