Wolfram Language & System 10.4 (2016)|Legacy Documentation

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constructs a dendrogram from the hierarchical clustering of the elements , , .

represents with in the constructed dendrogram.

represents with in the constructed dendrogram.

constructs an oriented dendrogram according to orientation.

Details and OptionsDetails and Options

  • The data elements can be numbers; numeric lists, matrices, or tensors; lists of Boolean elements; strings or images; geo positions or geographical entities; or colors. If the are lists, matrices, or tensors, each must have the same dimensions.
  • By default, Dendrogram is oriented from top to bottom. Possible orientations are: Top, Left, Right, and Bottom.
  • By default, the following distance functions are used for different types of elements:
  • ColorDistancecolors
    EuclideanDistancenumeric data
    GeoDistancegeospatial data/geographical entities
    JaccardDissimilarityBoolean data
  • Dendrogram has the same options as Graphics, with the following additions and changes:
  • ClusterDissimilarityFunctionAutomaticthe clustering linkage algorithm to use
    DistanceFunctionAutomaticthe distance or dissimilarity to use
  • ClusterDissimilarityFunction defines the intercluster dissimilarity, given the dissimilarities between member elements.
  • Possible settings for ClusterDissimilarityFunction include:
  • "Average"average intercluster dissimilarity
    "Centroid"distance from cluster centroids
    "Complete"largest intercluster dissimilarity
    "Median"distance from cluster medians
    "Single"smallest intercluster dissimilarity
    "Ward"Ward's minimum variance dissimilarity
    "WeightedAverage"weighted average intercluster dissimilarity
    a pure function
  • The function f defines a distance from any two clusters.
  • The function f needs to be a real-valued function of the DistanceMatrix.

ExamplesExamplesopen allclose all

Basic Examples  (3)Basic Examples  (3)

Obtain a dendrogram from a list of numbers:

Click for copyable input

Obtain a dendrogram from a list of cities and place the labels on the left:

Click for copyable input

Obtain a cluster hierarchy from a list of Boolean entries:

Click for copyable input
Introduced in 2016