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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