- sklearn.cluster.mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, max_iterations=300)¶
Perform MeanShift Clustering of data using a flat kernel
Seed using a binning technique for scalability.
X : array-like shape=[n_samples, n_features]
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.
cluster_centers : array [n_clusters, n_features]
Coordinates of cluster centers.
labels : array [n_samples]
Cluster labels for each point.
See examples/cluster/plot_meanshift.py for an example.