`sklearn.cluster`.mean_shift#

sklearn.cluster.mean_shift(X, *, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, max_iter=300, n_jobs=None)[source]#

Perform mean shift clustering of data using a flat kernel.

Read more in the User Guide.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

bandwidthfloat, default=None

Kernel bandwidth. If not None, must be in the range [0, +inf).

If None, the bandwidth is determined using a heuristic based on the median of all pairwise distances. This will take quadratic time in the number of samples. The sklearn.cluster.estimate_bandwidth function can be used to do this more efficiently.

seedsarray-like of shape (n_seeds, n_features) or None

Point used as initial kernel locations. If None and bin_seeding=False, each data point is used as a seed. If None and bin_seeding=True, see bin_seeding.

bin_seedingbool, default=False

If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to True will speed up the algorithm because fewer seeds will be initialized. Ignored if seeds argument is not None.

min_bin_freqint, default=1

To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds.

cluster_allbool, default=True

If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.

max_iterint, default=300

Maximum number of iterations, per seed point before the clustering operation terminates (for that seed point), if has not converged yet.

n_jobsint, default=None

The number of jobs to use for the computation. The following tasks benefit from the parallelization:

The search of nearest neighbors for bandwidth estimation and label assignments. See the details in the docstring of the NearestNeighbors class.
Hill-climbing optimization for all seeds.

See Glossary for more details.

None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

New in version 0.17: Parallel Execution using n_jobs.

Returns:

cluster_centersndarray of shape (n_clusters, n_features): Coordinates of cluster centers.
labelsndarray of shape (n_samples,): Cluster labels for each point.

Notes

For an example, see examples/cluster/plot_mean_shift.py.

Examples

>>> import numpy as np
>>> from sklearn.cluster import mean_shift
>>> X = np.array([[1, 1], [2, 1], [1, 0],
...               [4, 7], [3, 5], [3, 6]])
>>> cluster_centers, labels = mean_shift(X, bandwidth=2)
>>> cluster_centers
array([[3.33..., 6.     ],
       [1.33..., 0.66...]])
>>> labels
array([1, 1, 1, 0, 0, 0])

sklearn.cluster.mean_shift#

`sklearn.cluster`.mean_shift#