Graphs and Networks

Clustering Tree

Construct and visualize the hierarchical cluster of arbitrary data using the new ClusteringTree function in Version 11.

Cluster cities based on the proximity to one another.

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ClusteringTree[{Entity[ "City", {"London", "GreaterLondon", "UnitedKingdom"}], Entity["City", {"Paris", "IleDeFrance", "France"}], Entity["City", {"Chicago", "Illinois", "UnitedStates"}], Entity["City", {"Tokyo", "Tokyo", "Japan"}], Entity["City", {"Boston", "Massachusetts", "UnitedStates"}], Entity["City", {"Moscow", "Moscow", "Russia"}], Entity["City", {"SanDiego", "California", "UnitedStates"}], Entity["City", {"Baltimore", "Maryland", "UnitedStates"}]}]
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Obtain a cluster hierarchy from a list of colors.

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colors = RandomColor[18]
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ClusteringTree[colors, ClusterDissimilarityFunction -> "Centroid"]
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Choose a different GraphLayout.

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ClusteringTree[RandomColor[40], ClusterDissimilarityFunction -> "Centroid", GraphLayout -> "RadialDrawing"]
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Related Examples

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