8.1.2.6. sklearn.cluster.mean_shift¶
- sklearn.cluster.mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False, cluster_all=True, max_iterations=300)¶
Perform MeanShift Clustering of data using a flat kernel
Seed using a binning technique for scalability.
Parameters : X : array [n_samples, n_features]
Input points
bandwidth : float, optional
kernel bandwidth If bandwidth is not defined, it is set using a heuristic given by the median of all pairwise distances
seeds: array [n_seeds, n_features] :
point used as initial kernel locations
bin_seeding: boolean :
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. default value: False Ignored if seeds argument is not None
min_bin_freq: int, optional :
To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds. If not defined, set to 1.
Returns : cluster_centers : array [n_clusters, n_features]
Coordinates of cluster centers
labels : array [n_samples]
cluster labels for each point
Notes
See examples/plot_meanshift.py for an example.