ClusteringTree

ClusteringTree[{e1,e2,}]

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

ClusteringTree[{e1v1,e2v2,}]

represents ei with vi in the constructed graph.

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

represents ei with vi in the constructed graph.

ClusteringTree[label1e1,label2e2]

represents ei using labels labeli in the constructed graph.

ClusteringTree[data,h]

constructs a weighted tree from the hierarchical clustering of data by joining subclusters at distance less than h.

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; colors; as well as combinations of these. If the ei are lists, matrices, or tensors, each must have the same dimensions.
  • The result from ClusteringTree is a binary weighted tree, where the weight of each vertex indicates the distance between the two subtrees that have that vertex as root:
  • ClusteringTree has the same options as Graph, with the following additions and changes:
  • ClusterDissimilarityFunctionAutomaticthe clustering linkage algorithm to use
    DistanceFunctionAutomaticthe distance or dissimilarity to use
    EdgeStyleGrayLevel[0.65]styles for edges
    FeatureExtractorAutomatichow to extract features from data
    VertexSize0size of vertices
  • By default, ClusteringTree 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  (5)

Obtain a cluster hierarchy from a list of numbers:

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Unify clusters at distance less than 2:

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Obtain a cluster hierarchy from a list of strings:

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Obtain a cluster hierarchy from a list of images:

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Obtain a cluster hierarchy from a list of cities:

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Obtain a cluster hierarchy from a list of Boolean entries:

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Scope  (8)

Options  (3)

See Also

Dendrogram  FindClusters  ClusteringComponents  NearestNeighborGraph

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