# 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(
)
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 0.372 seconds)

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