66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
"""
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=============================================================
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Bisecting K-Means and Regular K-Means Performance Comparison
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=============================================================
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This example shows differences between Regular K-Means algorithm and Bisecting K-Means.
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While K-Means clusterings are different when increasing n_clusters,
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Bisecting K-Means clustering builds on top of the previous ones. As a result, it
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tends to create clusters that have a more regular large-scale structure. This
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difference can be visually observed: for all numbers of clusters, there is a
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dividing line cutting the overall data cloud in two for BisectingKMeans, which is not
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present for regular K-Means.
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"""
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import matplotlib.pyplot as plt
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from sklearn.cluster import BisectingKMeans, KMeans
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from sklearn.datasets import make_blobs
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print(__doc__)
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# Generate sample data
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n_samples = 10000
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random_state = 0
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X, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)
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# Number of cluster centers for KMeans and BisectingKMeans
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n_clusters_list = [4, 8, 16]
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# Algorithms to compare
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clustering_algorithms = {
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"Bisecting K-Means": BisectingKMeans,
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"K-Means": KMeans,
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}
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# Make subplots for each variant
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fig, axs = plt.subplots(
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len(clustering_algorithms), len(n_clusters_list), figsize=(12, 5)
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)
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axs = axs.T
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for i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):
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for j, n_clusters in enumerate(n_clusters_list):
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algo = Algorithm(n_clusters=n_clusters, random_state=random_state, n_init=3)
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algo.fit(X)
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centers = algo.cluster_centers_
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axs[j, i].scatter(X[:, 0], X[:, 1], s=10, c=algo.labels_)
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axs[j, i].scatter(centers[:, 0], centers[:, 1], c="r", s=20)
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axs[j, i].set_title(f"{algorithm_name} : {n_clusters} clusters")
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# Hide x labels and tick labels for top plots and y ticks for right plots.
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for ax in axs.flat:
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ax.label_outer()
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ax.set_xticks([])
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ax.set_yticks([])
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plt.show()
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