- sklearn.cluster.cluster_optics_xi(*, reachability, predecessor, ordering, min_samples, min_cluster_size=None, xi=0.05, predecessor_correction=True)¶
Automatically extract clusters according to the Xi-steep method.
- reachabilityndarray of shape (n_samples,)
Reachability distances calculated by OPTICS (
- predecessorndarray of shape (n_samples,)
Predecessors calculated by OPTICS.
- orderingndarray of shape (n_samples,)
OPTICS ordered point indices (
- 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_samplesconsecutive 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_samplesis 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
labelsdoes not reflect the hierarchy, usually
len(clusters) > np.unique(labels).