sklearn.cluster
.DBSCAN¶

class
sklearn.cluster.
DBSCAN
(eps=0.5, *, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None)[source]¶ Perform DBSCAN clustering from vector array or distance matrix.
DBSCAN  DensityBased Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.
Read more in the User Guide.
 Parameters
 epsfloat, default=0.5
The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function.
 min_samplesint, default=5
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.
 metricstring, or callable, default=’euclidean’
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 Glossary, in which case only “nonzero” elements may be considered neighbors for DBSCAN.New in version 0.17: metric precomputed to accept precomputed sparse matrix.
 metric_paramsdict, default=None
Additional keyword arguments for the metric function.
New in version 0.19.
 algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’
The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. See NearestNeighbors module documentation for details.
 leaf_sizeint, 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.
 pfloat, default=None
The power of the Minkowski metric to be used to calculate distance between points.
 n_jobsint, default=None
The number of parallel jobs to run.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
 Attributes
 core_sample_indices_ndarray of shape (n_core_samples,)
Indices of core samples.
 components_ndarray of shape (n_core_samples, n_features)
Copy of each core sample found by training.
 labels_ndarray of shape (n_samples)
Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label 1.
See also
OPTICS
A similar clustering at multiple values of eps. Our implementation is optimized for memory usage.
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.cluster.OPTICS
provides a similar clustering with lower memory usage.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
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 19.
Examples
>>> from sklearn.cluster import DBSCAN >>> import numpy as np >>> X = np.array([[1, 2], [2, 2], [2, 3], ... [8, 7], [8, 8], [25, 80]]) >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) >>> clustering.labels_ array([ 0, 0, 0, 1, 1, 1]) >>> clustering DBSCAN(eps=3, min_samples=2)
Methods
fit
(self, X[, y, sample_weight])Perform DBSCAN clustering from features, or distance matrix.
fit_predict
(self, X[, y, sample_weight])Perform DBSCAN clustering from features or distance matrix, and return cluster labels.
get_params
(self[, deep])Get parameters for this estimator.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, eps=0.5, *, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y=None, sample_weight=None)[source]¶ Perform DBSCAN clustering from features, or distance matrix.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)
Training instances to cluster, or distances between instances if
metric='precomputed'
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
. sample_weightarraylike of shape (n_samples,), default=None
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 a negative weight may inhibit its epsneighbor from being core. Note that weights are absolute, and default to 1. yIgnored
Not used, present here for API consistency by convention.
 Returns
 self

fit_predict
(self, X, y=None, sample_weight=None)[source]¶ Perform DBSCAN clustering from features or distance matrix, and return cluster labels.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples)
Training instances to cluster, or distances between instances if
metric='precomputed'
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
. sample_weightarraylike of shape (n_samples,), default=None
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 a negative weight may inhibit its epsneighbor from being core. Note that weights are absolute, and default to 1. yIgnored
Not used, present here for API consistency by convention.
 Returns
 labelsndarray of shape (n_samples,)
Cluster labels. Noisy samples are given the label 1.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.