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 - Density-Based 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.
This implementation has a worst case memory complexity of \(O({n}^2)\), which can occur when the
eps
param is large andmin_samples
is low, while the original DBSCAN only uses linear memory. For further details, see the Notes below.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. If
min_samples
is set to a higher value, DBSCAN will find denser clusters, whereas if it is set to a lower value, the found clusters will be more sparse.- metricstr, 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 sparse graph, 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. If None, then
p=2
(equivalent to the Euclidean distance).- 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.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
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 bulk-computes 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 pre-compute 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.OPTICS
provides a similar clustering with lower memory usage.References
Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based 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. 226-231. 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
(X[, y, sample_weight])Perform DBSCAN clustering from features, or distance matrix.
fit_predict
(X[, y, sample_weight])Compute clusters from a data or distance matrix and predict labels.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
- fit(X, y=None, sample_weight=None)[source]¶
Perform DBSCAN clustering from features, or distance matrix.
- Parameters:
- X{array-like, 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
.- yIgnored
Not used, present here for API consistency by convention.
- sample_weightarray-like 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 eps-neighbor from being core. Note that weights are absolute, and default to 1.
- Returns:
- selfobject
Returns a fitted instance of self.
- fit_predict(X, y=None, sample_weight=None)[source]¶
Compute clusters from a data or distance matrix and predict labels.
- Parameters:
- X{array-like, 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
.- yIgnored
Not used, present here for API consistency by convention.
- sample_weightarray-like 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 eps-neighbor from being core. Note that weights are absolute, and default to 1.
- Returns:
- labelsndarray of shape (n_samples,)
Cluster labels. Noisy samples are given the label -1.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(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:
- paramsdict
Parameter names mapped to their values.
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') DBSCAN [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). 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:
- selfestimator instance
Estimator instance.
Examples using sklearn.cluster.DBSCAN
¶
Comparing different clustering algorithms on toy datasets
Demo of DBSCAN clustering algorithm
Demo of HDBSCAN clustering algorithm