sklearn.cluster
.dbscan¶

sklearn.cluster.
dbscan
(X, eps=0.5, min_samples=5, metric=’minkowski’, metric_params=None, algorithm=’auto’, leaf_size=30, p=2, sample_weight=None, n_jobs=1)[source]¶ Perform DBSCAN clustering from vector array or distance matrix.
Read more in the User Guide.
Parameters:  X : array or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples)
A feature array, or array of distances between samples if
metric='precomputed'
. eps : float, optional
The maximum distance between two samples for them to be considered as in the same neighborhood.
 min_samples : int, optional
The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
 metric : string, or callable
The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by
sklearn.metrics.pairwise_distances
for its metric parameter. If metric is “precomputed”, X is assumed to be a distance matrix and must be square. X may be a sparse matrix, in which case only “nonzero” elements may be considered neighbors for DBSCAN. metric_params : dict, optional
Additional keyword arguments for the metric function.
New in version 0.19.
 algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. See NearestNeighbors module documentation for details.
 leaf_size : int, optional (default = 30)
Leaf size passed to BallTree or cKDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
 p : float, optional
The power of the Minkowski metric to be used to calculate distance between points.
 sample_weight : array, shape (n_samples,), optional
Weight of each sample, such that a sample with a weight of at least
min_samples
is by itself a core sample; a sample with negative weight may inhibit its epsneighbor from being core. Note that weights are absolute, and default to 1. n_jobs : int, optional (default = 1)
The number of parallel jobs to run for neighbors search. If
1
, then the number of jobs is set to the number of CPU cores.
Returns:  core_samples : array [n_core_samples]
Indices of core samples.
 labels : array [n_samples]
Cluster labels for each point. Noisy samples are given the label 1.
Notes
For an example, see examples/cluster/plot_dbscan.py.
This implementation bulkcomputes all neighborhood queries, which increases the memory complexity to O(n.d) where d is the average number of neighbors, while original DBSCAN had memory complexity O(n). It may attract a higher memory complexity when querying these nearest neighborhoods, depending on the
algorithm
.One way to avoid the query complexity is to precompute sparse neighborhoods in chunks using
NearestNeighbors.radius_neighbors_graph
withmode='distance'
, then usingmetric='precomputed'
here.Another way to reduce memory and computation time is to remove (near)duplicate points and use
sample_weight
instead.References
Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A DensityBased Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226231. 1996