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ClusteringTree—Wolfram Language Documentation

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• See Also
  • Dendrogram
  • FindClusters
  • ClusteringComponents
  • NearestNeighborGraph
• Related Guides
  • Graph Construction & Representation
  • Cluster Analysis
  • Data Visualization
  • Distance and Similarity Measures
  • Statistical Data Analysis
  • Discrete Mathematics
  • Natural Language Processing
  • Text Analysis
  • Sequence Alignment & Comparison
  • Unsupervised Machine Learning
  • Tree Construction & Representation

ClusteringTree[{e₁,e₂,…}]

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

ClusteringTree[{e₁v₁,e₂v₂,…}]

represents e_i with v_i in the constructed graph.

ClusteringTree[{e₁,e₂,…}{v₁,v₂,…}]

represents e_i with v_i in the constructed graph.

ClusteringTree[label₁e₁,label₂e_2…]

represents e_i using labels label_i 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.

Obtain a cluster hierarchy from a list of numbers:

Unify clusters at distance less than 2:

Obtain a cluster hierarchy from a list of strings:

Obtain a cluster hierarchy from a list of images:

Obtain a cluster hierarchy from a list of cities:

Obtain a cluster hierarchy from a list of Boolean entries:

Obtain a cluster hierarchy from a list of numbers:

Obtain the leaves' labels:

Look at the distance between subclusters by looking at the VertexWeight:

Find the shortest path from the root vertex to the leaf 3.4:

Obtain a cluster hierarchy from a heterogeneous dataset:

Compare it with the cluster hierarchy of the colors:

Generate a list of random colors:

Obtain a cluster hierarchy from the list using the "Centroid" linkage:

Compute the hierarchical clustering from an Association:

Compare it with the hierarchical clustering of its Values:

Compare it with the hierarchical clustering of its Keys:

Obtain a cluster hierarchy by merging clusters at distance less than 0.4:

Change the style and the layout of the ClusteringTree:

Obtain a cluster hierarchy from a list of three-dimensional vectors and label the leaves with the total of the corresponding element:

Compare it with the cluster hierarchy of the total of each vector:

Obtain a cluster hierarchy from a list of integers:

Change the vertex labels by using regular polygons:

Generate a list of random colors:

Obtain a cluster hierarchy from the list using the "Centroid" linkage:

Obtain a cluster hierarchy from the list using the "Single" linkage:

Obtain a cluster hierarchy from the list using a different "ClusterDissimilarityFunction":

Generate a list of random vectors:

Obtain a cluster hierarchy using different DistanceFunction:

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