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# Mean-Shift Clustering Algorithm in Scikit-learn

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### Version¶

In [1]:
import sklearn
sklearn.__version__

Out[1]:
'0.18'

### Imports¶

This tutorial imports MeanShift, estimate_bandwidth, make_blobs.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go
import plotly

import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.datasets.samples_generator import make_blobs

Automatically created module for IPython interactive environment


### Calculations¶

Generate sample data.

In [3]:
centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)


Compute clustering with MeanShift.

In [4]:
# The following bandwidth can be automatically detected using
bandwidth = 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_)

number of estimated clusters : 3


### Plot Results¶

In [5]:
colors = ['blue','green','red','cyan','magenta']
cluster = [ ]
for k, col in zip(range(n_clusters_), colors):
my_members = labels == k
cluster_center = cluster_centers[k]
trace = go.Scatter(x=X[my_members, 0], y=X[my_members, 1],
mode='markers', showlegend=False,
marker= dict(color=col, size=10,))

trace1 = go.Scatter(x=[cluster_center[0]], y=[cluster_center[1]],
mode='markers',  showlegend=False,
marker=dict(color=col, size=20,
line=dict(color = 'black',
width = 1)))
cluster.append(trace)
cluster.append(trace1)

layout = go.Layout(title = 'Estimated number of clusters: %d' % n_clusters_,
xaxis = dict(showgrid = False, zeroline = False),
yaxis = dict(showgrid = False, zeroline = False),)

fig = go.Figure(data = cluster, layout = layout)
py.iplot(fig)

Out[5]:

### 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.