Intercluster distance maps display an embedding of the cluster centers in 2 dimensions with the distance to other centers preserved. E.g. the closer to centers are in the visualization, the closer they are in the original feature space. The clusters are sized according to a scoring metric. By default, they are sized by membership, e.g. the number of instances that belong to each center. This gives a sense of the relative importance of clusters. Note however, that because two clusters overlap in the 2D space, it does not imply that they overlap in the original feature space.
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from sklearn.cluster import KMeans from sklearn.datasets import make_blobs
from yellowbrick.cluster import InterclusterDistance
# Generate synthetic dataset with 12 random clusters X, y = make_blobs(n_samples=1000, n_features=12, centers=12, random_state=42)
# Instantiate the clustering model and visualizer model = KMeans(6) visualizer = InterclusterDistance(model)
visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure
The same functionality above can be achieved with the associated quick method intercluster_distance. This method will build the InterclusterDistance object with the associated arguments, fit it, then (optionally) immediately show it.
from yellowbrick.datasets import load_nfl from sklearn.cluster import MiniBatchKMeans from yellowbrick.cluster import intercluster_distance
X, _ = load_nfl() intercluster_distance(MiniBatchKMeans(5, random_state=777), X)
yellowbrick.cluster.icdm