# 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: reachability : array, shape (n_samples,) Reachability distances calculated by OPTICS (reachability_) predecessor : array, shape (n_samples,) Predecessors calculated by OPTICS. ordering : array, shape (n_samples,) OPTICS ordered point indices (ordering_) min_samples : int > 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_size : int > 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. xi : float, 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_correction : bool, optional (default=True) Correct clusters based on the calculated predecessors. labels : array, shape (n_samples) The labels assigned to samples. Points which are not included in any cluster are labeled as -1. clusters : array, 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).