Reference:
Dorin Comaniciu and Peter Meer, ?Mean Shift: A robust approach toward feature space analysis?. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619.
1 2 3 4 5 | print(__doc__)import numpy as npfrom sklearn.cluster import MeanShift, estimate_bandwidthfrom sklearn.datasets.samples_generator import make_blobs |
Generate sample data
1 2 | centers = [[1, 1], [-1, -1], [1, -1]]X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6) |
Compute clustering with MeanShift
1 2 3 4 5 6 7 8 9 10 11 12 | # The following bandwidth can be automatically detected usingbandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)ms.fit(X)labels = ms.labels_cluster_centers = ms.cluster_centers_labels_unique = np.unique(labels)n_clusters_ = len(labels_unique)print("number of estimated clusters : %d" % n_clusters_) |
Out:
1 | number of estimated clusters : 3 |
Plot result
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | import matplotlib.pyplot as pltfrom itertools import cycleplt.figure(1)plt.clf()colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')for k, col in zip(range(n_clusters_), colors): my_members = labels == k cluster_center = cluster_centers[k] plt.plot(X[my_members, 0], X[my_members, 1], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)plt.title('Estimated number of clusters: %d' % n_clusters_)plt.show() |
Total running time of the script: (0 minutes 1.117 seconds)
Download Python source code:
plot_mean_shift.py
Download IPython notebook:
plot_mean_shift.ipynb
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