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Bisecting K-Means and Regular K-Means Performance Comparison#

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

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.

Bisecting K-Means : 4 clusters, Bisecting K-Means : 8 clusters, Bisecting K-Means : 16 clusters, K-Means : 4 clusters, K-Means : 8 clusters, K-Means : 16 clusters

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
fromsklearn.clusterimport BisectingKMeans , KMeans
fromsklearn.datasetsimport make_blobs
print(__doc__)
# Generate sample data
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 = [4, 8, 16]
# Algorithms to compare
clustering_algorithms = {
 "Bisecting K-Means": BisectingKMeans ,
 "K-Means": KMeans ,
}
# Make subplots for each variant
fig, axs = plt.subplots (
 len(clustering_algorithms), len(n_clusters_list), figsize=(12, 5)
)
axs = axs.T
for i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):
 for j, n_clusters in enumerate(n_clusters_list):
 algo = Algorithm(n_clusters=n_clusters, random_state=random_state, n_init=3)
 algo.fit(X)
 centers = algo.cluster_centers_
 axs[j, i].scatter(X[:, 0], X[:, 1], s=10, c=algo.labels_)
 axs[j, i].scatter(centers[:, 0], centers[:, 1], c="r", s=20)
 axs[j, i].set_title(f"{algorithm_name} : {n_clusters} clusters")
# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
 ax.label_outer()
 ax.set_xticks([])
 ax.set_yticks([])
plt.show ()