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.
In[1]:=
![Click for copyable input](assets.en/clustering-tree/In_21.png)
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"}]}]
Out[1]=
![](assets.en/clustering-tree/O_16.png)
Obtain a cluster hierarchy from a list of colors.
In[2]:=
![Click for copyable input](assets.en/clustering-tree/In_22.png)
colors = RandomColor[18]
Out[2]=
![](assets.en/clustering-tree/O_17.png)
In[3]:=
![Click for copyable input](assets.en/clustering-tree/In_23.png)
ClusteringTree[colors, ClusterDissimilarityFunction -> "Centroid"]
Out[3]=
![](assets.en/clustering-tree/O_18.png)
Choose a different GraphLayout.
In[4]:=
![Click for copyable input](assets.en/clustering-tree/In_24.png)
ClusteringTree[RandomColor[40],
ClusterDissimilarityFunction -> "Centroid",
GraphLayout -> "RadialDrawing"]
Out[4]=
![](assets.en/clustering-tree/O_19.png)