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DOC: more didactic examples of bisecting kmeans. #25494

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Jan 27, 2023
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DOC: more didactic examples of bisecting kmeans. #25494

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DOC: more didactice examples of bisecting kmeans.
GaelVaroquaux Jan 26, 2023
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adjust fig aspect ratio
GaelVaroquaux Jan 26, 2023
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Update examples/cluster/plot_bisect_kmeans.py
GaelVaroquaux Jan 27, 2023
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Update examples/cluster/plot_bisect_kmeans.py
GaelVaroquaux Jan 27, 2023
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Update examples/cluster/plot_bisect_kmeans.py
GaelVaroquaux Jan 27, 2023
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Update examples/cluster/plot_bisect_kmeans.py
GaelVaroquaux Jan 27, 2023
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Remove reference to Voronoi
GaelVaroquaux Jan 27, 2023
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Update examples/cluster/plot_bisect_kmeans.py
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16 changes: 9 additions & 7 deletions examples/cluster/plot_bisect_kmeans.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,12 @@

This example shows differences between Regular K-Means algorithm and Bisecting K-Means.

While K-Means clusterings are different when with increasing n_clusters,
Bisecting K-Means clustering build on top of the previous ones.

This difference can visually be observed.
While K-Means clusterings are different when increasing n_clusters,
Bisecting K-Means clustering builds on top of the previous ones. As a result, it
tends to create clusters that have a more regular large-scale structure. This
difference can be visually observed: for all numbers of clusters, there is a
dividing line cutting the overall data cloud in two for BisectingKMeans, which is not
present for regular K-Means.

"""
import matplotlib.pyplot as plt
Expand All @@ -21,13 +23,13 @@


# Generate sample data
n_samples = 1000
n_samples = 10000
random_state = 0

X, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)

# Number of cluster centers for KMeans and BisectingKMeans
n_clusters_list = [2, 3, 4, 5]
n_clusters_list = [4, 8, 16]

# Algorithms to compare
clustering_algorithms = {
Expand All @@ -37,7 +39,7 @@

# Make subplots for each variant
fig, axs = plt.subplots(
len(clustering_algorithms), len(n_clusters_list), figsize=(15, 5)
len(clustering_algorithms), len(n_clusters_list), figsize=(12, 5)
)

axs = axs.T
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