System'

Dendrogram

Dendrogram[{e1,e2,}]

constructs a dendrogram from the hierarchical clustering of the elements e1, e2, .

Dendrogram[{e1v1,e2v2,}]

represents ei with vi in the constructed dendrogram.

Dendrogram[{e1,e2,}{v1,v2,}]

represents ei with vi in the constructed dendrogram.

Dendrogram[label1e1,label2e2,]

represents ei using labels labeli in the constructed dendrogram.

Dendrogram[data,orientation]

constructs an oriented dendrogram according to orientation.

Dendrogram[tree]

constructs the dendrogram corresponding to weighted tree tree.

Details and Options

  • The data elements ei can be numbers; numeric lists, matrices, or tensors; lists of Boolean elements; strings or images; geo positions or geographical entities; and colors, as well as combinations of these. If the ei 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.
  • Trees on which to compute Dendrogram can only be weighted on vertices.
  • 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
    FeatureExtractorAutomatichow to extract features from data
  • Dendrogram evaluated on a weighted tree only displays the graph as a dendrogram, therefore only the options of Graphics will change the final result.
  • By default, Dendrogram will preprocess the data automatically unless either a DistanceFunction or a FeatureExtractor is specified.
  • 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.

Examples

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Basic Examples  (4)

Obtain a dendrogram from a list of numbers:

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Obtain a dendrogram from a weighted tree:

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

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Obtain a cluster hierarchy from a list of Boolean entries:

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Scope  (7)

Options  (3)

Applications  (1)

See Also

ClusteringTree  FindClusters  ClusteringComponents  TreePlot

Introduced in 2016
(10.4)
| Updated in 2017
(11.1)