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# `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)

  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.

  The worst case memory complexity of DBSCAN is \(O({n}^2)\), which can occur when the `eps` param is large and `min_samples` is low.

  Read more in the User Guide.

  Parameters:
    eps float, 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_samples int, 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.

    metric str, 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_params dict, 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_size int, 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, 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_jobs int, default=None

      The number of parallel jobs to run. `None` means 1 unless in a `joblib.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` with `mode='distance'`, then using `metric='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`() | 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)

    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 sparse `csr_matrix`.

      y Ignored

        Not used, present here for API consistency by convention.

      sample_weight array-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:
      self object

        Returns a fitted instance of self.

  fit_predict(X, y=None, sample_weight=None)

    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 sparse `csr_matrix`.

      y Ignored

        Not used, present here for API consistency by convention.

      sample_weight array-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:
      labels ndarray of shape (n_samples,)

        Cluster labels. Noisy samples are given the label -1.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DBSCAN

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

## Examples using `sklearn.cluster.DBSCAN`

Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm