sklearn.cluster.cluster_optics_xi¶
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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
- reachabilityarray, shape (n_samples,)
Reachability distances calculated by OPTICS (
reachability_)- predecessorarray, shape (n_samples,)
Predecessors calculated by OPTICS.
- orderingarray, 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_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 ofmin_samplesis used instead.- xifloat, between 0 and 1, optional (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, optional (default=True)
Correct clusters based on the calculated predecessors.
- Returns
- labelsarray, shape (n_samples)
The labels assigned to samples. Points which are not included in any cluster are labeled as -1.
- clustersarray, 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. Sincelabelsdoes not reflect the hierarchy, usuallylen(clusters) > np.unique(labels).