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Demo of affinity propagation clustering algorithm#

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
importnumpyasnp
fromsklearnimport metrics
fromsklearn.clusterimport AffinityPropagation
fromsklearn.datasetsimport 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#

af = AffinityPropagation (preference=-50, random_state=0).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_
n_clusters_ = len(cluster_centers_indices)
print("Estimated number of clusters: %d" % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score (labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score (labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score (labels_true, labels))
print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score (labels_true, labels))
print(
 "Adjusted Mutual Information: %0.3f"
 % metrics.adjusted_mutual_info_score (labels_true, labels)
)
print(
 "Silhouette Coefficient: %0.3f"
 % metrics.silhouette_score (X, labels, metric="sqeuclidean")
)

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#

importmatplotlib.pyplotasplt
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 ()