sklearn.cluster
.cluster_optics_xi¶
- 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.
- Parameters:
- 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 ofmin_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.
- Returns:
- 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. Sincelabels
does not reflect the hierarchy, usuallylen(clusters) > np.unique(labels)
.