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 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

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

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()
Estimated number of clusters: 3

Total running time of the script: (0 minutes 0.375 seconds)

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