Demo of affinity propagation clustering algorithm (original) (raw)
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Reference: Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
Authors: The scikit-learn developers
SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from sklearn import metrics from sklearn.cluster import AffinityPropagation from sklearn.datasets import make_blobs
Generate sample data#
centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs( n_samples=300, centers=centers, cluster_std=0.5, random_state=0 )
Compute Affinity Propagation#
Estimated number of clusters: 3 Homogeneity: 0.872 Completeness: 0.872 V-measure: 0.872 Adjusted Rand Index: 0.912 Adjusted Mutual Information: 0.871 Silhouette Coefficient: 0.753
Plot result#
import matplotlib.pyplot as plt
plt.close("all") plt.figure(1) plt.clf()
colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, 4)))
for k, col in zip(range(n_clusters_), colors): class_members = labels == k cluster_center = X[cluster_centers_indices[k]] plt.scatter( X[class_members, 0], X[class_members, 1], color=col["color"], marker="." ) plt.scatter( cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o" ) for x in X[class_members]: plt.plot( [cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"] )
plt.title("Estimated number of clusters: %d" % n_clusters_) plt.show()
Total running time of the script: (0 minutes 0.302 seconds)
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