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# `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=None)

  Perform DBSCAN clustering from vector array or distance matrix.

  Read more in the User Guide.

  Parameters:
    X{array-like, sparse (CSR) matrix} of shape (n_samples, n_features) or (n_samples, n_samples)

      A feature array, or array of distances between samples if `metric='precomputed'`.

    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.

    metric str or callable, default=’minkowski’

      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 during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors.

    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=2

      The power of the Minkowski metric to be used to calculate distance between points.

    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 negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.

    n_jobs int, default=None

      The number of parallel jobs to run for neighbors search. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details. If precomputed distance are used, parallel execution is not available and thus n_jobs will have no effect.

  Returns:
    core_samples ndarray of shape (n_core_samples,)

      Indices of core samples.

    labels ndarray of shape (n_samples,)

      Cluster labels for each point. Noisy samples are given the label -1.

  See also

  `DBSCAN`

    An estimator interface for this clustering algorithm.

  `OPTICS`

    A similar estimator interface 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.