class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=None)
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Mean shift clustering using a flat kernel.
Mean shift clustering aims to discover “blobs” in a smooth density of samples. It is a centroidbased algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a postprocessing stage to eliminate nearduplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
Read more in the User Guide.
Parameters: 


Attributes: 

Scalability:
Because this implementation uses a flat kernel and a Ball Tree to look up members of each kernel, the complexity will tend towards O(T*n*log(n)) in lower dimensions, with n the number of samples and T the number of points. In higher dimensions the complexity will tend towards O(T*n^2).
Scalability can be boosted by using fewer seeds, for example by using a higher value of min_bin_freq in the get_bin_seeds function.
Note that the estimate_bandwidth function is much less scalable than the mean shift algorithm and will be the bottleneck if it is used.
Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603619.
>>> from sklearn.cluster import MeanShift >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> clustering = MeanShift(bandwidth=2).fit(X) >>> clustering.labels_ array([1, 1, 1, 0, 0, 0]) >>> clustering.predict([[0, 0], [5, 5]]) array([1, 0]) >>> clustering MeanShift(bandwidth=2, bin_seeding=False, cluster_all=True, min_bin_freq=1, n_jobs=None, seeds=None)
fit (X[, y])  Perform clustering. 
fit_predict (X[, y])  Performs clustering on X and returns cluster labels. 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict the closest cluster each sample in X belongs to. 
set_params (**params)  Set the parameters of this estimator. 
__init__(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=None)
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fit(X, y=None)
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Perform clustering.
Parameters: 


fit_predict(X, y=None)
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Performs clustering on X and returns cluster labels.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Predict the closest cluster each sample in X belongs to.
Parameters: 


Returns: 

set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.cluster.MeanShift
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.cluster.MeanShift.html