.. _sphx_glr_auto_examples_cluster_plot_dbscan.py:


===================================
Demo of DBSCAN clustering algorithm
===================================

Finds core samples of high density and expands clusters from them.



.. code-block:: python

    print(__doc__)

    import numpy as np

    from sklearn.cluster import DBSCAN
    from sklearn import metrics
    from sklearn.datasets.samples_generator import make_blobs
    from sklearn.preprocessing import StandardScaler








Generate sample data


.. code-block:: python

    centers = [[1, 1], [-1, -1], [1, -1]]
    X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                                random_state=0)

    X = StandardScaler().fit_transform(X)







Compute DBSCAN


.. code-block:: python

    db = DBSCAN(eps=0.3, min_samples=10).fit(X)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

    print('Estimated number of clusters: %d' % n_clusters_)
    print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    print("Adjusted Rand Index: %0.3f"
          % metrics.adjusted_rand_score(labels_true, labels))
    print("Adjusted Mutual Information: %0.3f"
          % metrics.adjusted_mutual_info_score(labels_true, labels))
    print("Silhouette Coefficient: %0.3f"
          % metrics.silhouette_score(X, labels))





.. rst-class:: sphx-glr-script-out

 Out::

      Estimated number of clusters: 3
    Homogeneity: 0.953
    Completeness: 0.883
    V-measure: 0.917
    Adjusted Rand Index: 0.952
    Adjusted Mutual Information: 0.883
    Silhouette Coefficient: 0.626


Plot result


.. code-block:: python

    import matplotlib.pyplot as plt

    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = [plt.cm.Spectral(each)
              for each in np.linspace(0, 1, len(unique_labels))]
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = [0, 0, 0, 1]

        class_member_mask = (labels == k)

        xy = X[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                 markeredgecolor='k', markersize=14)

        xy = X[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                 markeredgecolor='k', markersize=6)

    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()



.. image:: /auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png
    :align: center




**Total running time of the script:**
(0 minutes 0.116 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_dbscan.py <plot_dbscan.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_dbscan.ipynb <plot_dbscan.ipynb>`