Warning: This documentation is for scikits.learn version 0.6.0. — Latest stable version

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6.8.2. scikits.learn.cluster.MeanShift

class scikits.learn.cluster.MeanShift(bandwidth=None)

MeanShift clustering

Parameters :

bandwidth: float, optional :

Bandwith used in the RBF kernel If not set, the bandwidth is estimated. See clustering.estimate_bandwidth

Notes

Reference:

K. Funkunaga and L.D. Hosteler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition”

The algorithmic complexity of the mean shift algorithm is O(T n^2) with n the number of samples and T the number of iterations. It is not adviced for a large number of samples.

Attributes

cluster_centers_: array, [n_clusters, n_features] Coordinates of cluster centers
labels_: Labels of each point

Methods

fit(X): Compute MeanShift clustering
__init__(bandwidth=None)
fit(X, **params)

Compute MeanShift

Parameters :

X : array [n_samples, n_features]

Input points