cluster_optics_dbscan (original) (raw)
sklearn.cluster.cluster_optics_dbscan(*, reachability, core_distances, ordering, eps)[source]#
Perform DBSCAN extraction for an arbitrary epsilon.
Extracting the clusters runs in linear time. Note that this results inlabels_ which are close to a DBSCAN with similar settings and eps, only if eps is close to max_eps.
Parameters:
reachabilityndarray of shape (n_samples,)
Reachability distances calculated by OPTICS (reachability_).
core_distancesndarray of shape (n_samples,)
Distances at which points become core (core_distances_).
orderingndarray of shape (n_samples,)
OPTICS ordered point indices (ordering_).
epsfloat
DBSCAN eps parameter. Must be set to < max_eps. Results will be close to DBSCAN algorithm if eps and max_eps are close to one another.
Returns:
**labels_**array of shape (n_samples,)
The estimated labels.
Examples
import numpy as np from sklearn.cluster import cluster_optics_dbscan, compute_optics_graph X = np.array([[1, 2], [2, 5], [3, 6], ... [8, 7], [8, 8], [7, 3]]) ordering, core_distances, reachability, predecessor = compute_optics_graph( ... X, ... min_samples=2, ... max_eps=np.inf, ... metric="minkowski", ... p=2, ... metric_params=None, ... algorithm="auto", ... leaf_size=30, ... n_jobs=None, ... ) eps = 4.5 labels = cluster_optics_dbscan( ... reachability=reachability, ... core_distances=core_distances, ... ordering=ordering, ... eps=eps, ... ) labels array([0, 0, 0, 1, 1, 1])