sklearn.cluster.cluster_optics_xi(*, reachability, predecessor, ordering, min_samples, min_cluster_size=None, xi=0.05, predecessor_correction=True)[source]

Automatically extract clusters according to the Xi-steep method.

reachabilityndarray of shape (n_samples,)

Reachability distances calculated by OPTICS (reachability_)

predecessorndarray of shape (n_samples,)

Predecessors calculated by OPTICS.

orderingndarray of shape (n_samples,)

OPTICS ordered point indices (ordering_)

min_samplesint > 1 or float between 0 and 1

The same as the min_samples given to OPTICS. Up and down steep regions can’t have more then min_samples consecutive non-steep points. Expressed as an absolute number or a fraction of the number of samples (rounded to be at least 2).

min_cluster_sizeint > 1 or float between 0 and 1, default=None

Minimum number of samples in an OPTICS cluster, expressed as an absolute number or a fraction of the number of samples (rounded to be at least 2). If None, the value of min_samples is used instead.

xifloat between 0 and 1, default=0.05

Determines the minimum steepness on the reachability plot that constitutes a cluster boundary. For example, an upwards point in the reachability plot is defined by the ratio from one point to its successor being at most 1-xi.

predecessor_correctionbool, default=True

Correct clusters based on the calculated predecessors.

labelsndarray of shape (n_samples,)

The labels assigned to samples. Points which are not included in any cluster are labeled as -1.

clustersndarray of shape (n_clusters, 2)

The list of clusters in the form of [start, end] in each row, with all indices inclusive. The clusters are ordered according to (end, -start) (ascending) so that larger clusters encompassing smaller clusters come after such nested smaller clusters. Since labels does not reflect the hierarchy, usually len(clusters) > np.unique(labels).