[Scikit-learn-general] drawing ellipses around clusters in mean shift clustering

[gea]

Hi,

 

I am working with an implementation of the mean shift algorithm using sklearn.cluster from

http://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html

and would like to compute the ellipses around the clusters.

Is there an implementation for this?

Thanks

"""
=============================================
A demo of the mean-shift clustering algorithm
=============================================

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.

"""
print(__doc__)

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

import numpy as np
from read_data import read_data

slide = 15

data = read_data()

points = data[slide]
X = np.array(points)


###############################################################################
# Generate sample datacenters = [[1, 1], [-1, -1], [1, -1]]
#, _ = make_blobs(n_samples=1000, centers=centers, cluster_std=0.6)

###############################################################################
# Compute clustering with MeanShift

# print bandwidth

bandwidth = 100

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_)

###############################################################################
# Plot result
import matplotlib.pyplot as plt
from itertools import cycle

plt.figure(1)
plt.clf()

plt.axis([0, 1024, 0, 768])


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.show()



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