# Demo of affinity propagation clustering algorithm¶

Reference: Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007

import numpy as np

from sklearn.cluster import AffinityPropagation
from sklearn import metrics
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¶

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(
)
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
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 0.342 seconds)

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