.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_affinity_propagation.py: ================================================= Demo of affinity propagation clustering algorithm ================================================= Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Points", Science Feb. 2007 .. image:: /auto_examples/cluster/images/sphx_glr_plot_affinity_propagation_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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 | .. code-block:: default print(__doc__) 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).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')) # ############################################################################# # Plot result import matplotlib.pyplot as plt from itertools import cycle plt.close('all') plt.figure(1) plt.clf() colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk') for k, col in zip(range(n_clusters_), colors): class_members = labels == k cluster_center = X[cluster_centers_indices[k]] plt.plot(X[class_members, 0], X[class_members, 1], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) for x in X[class_members]: plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col) plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.665 seconds) **Estimated memory usage:** 8 MB .. _sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_affinity_propagation.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_affinity_propagation.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_