.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_dbscan.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_dbscan.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_cluster_plot_dbscan.py:
===================================
Demo of DBSCAN clustering algorithm
===================================
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core
samples in regions of high density and expands clusters from them. This
algorithm is good for data which contains clusters of similar density.
See the :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` example
for a demo of different clustering algorithms on 2D datasets.
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Data generation
---------------
We use :class:`~sklearn.datasets.make_blobs` to create 3 synthetic clusters.
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.. code-block:: Python
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
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)
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We can visualize the resulting data:
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.. code-block:: Python
import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1])
plt.show()
.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png
:alt: plot dbscan
:srcset: /auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png
:class: sphx-glr-single-img
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Compute DBSCAN
--------------
One can access the labels assigned by :class:`~sklearn.cluster.DBSCAN` using
the `labels_` attribute. Noisy samples are given the label math:`-1`.
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.. code-block:: Python
import numpy as np
from sklearn import metrics
from sklearn.cluster import DBSCAN
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
print("Estimated number of clusters: %d" % n_clusters_)
print("Estimated number of noise points: %d" % n_noise_)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Estimated number of clusters: 3
Estimated number of noise points: 18
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Clustering algorithms are fundamentally unsupervised learning methods.
However, since :class:`~sklearn.datasets.make_blobs` gives access to the true
labels of the synthetic clusters, it is possible to use evaluation metrics
that leverage this "supervised" ground truth information to quantify the
quality of the resulting clusters. Examples of such metrics are the
homogeneity, completeness, V-measure, Rand-Index, Adjusted Rand-Index and
Adjusted Mutual Information (AMI).
If the ground truth labels are not known, evaluation can only be performed
using the model results itself. In that case, the Silhouette Coefficient comes
in handy.
For more information, see the
:ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`
example or the :ref:`clustering_evaluation` module.
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.. code-block:: Python
print(f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):.3f}")
print(f"Completeness: {metrics.completeness_score(labels_true, labels):.3f}")
print(f"V-measure: {metrics.v_measure_score(labels_true, labels):.3f}")
print(f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):.3f}")
print(
"Adjusted Mutual Information:"
f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}"
)
print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}")
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Homogeneity: 0.953
Completeness: 0.883
V-measure: 0.917
Adjusted Rand Index: 0.952
Adjusted Mutual Information: 0.916
Silhouette Coefficient: 0.626
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Plot results
------------
Core samples (large dots) and non-core samples (small dots) are color-coded
according to the assigned cluster. Samples tagged as noise are represented in
black.
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.. code-block:: Python
unique_labels = set(labels)
core_samples_mask = np.zeros_like(labels, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
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(f"Estimated number of clusters: {n_clusters_}")
plt.show()
.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png
:alt: Estimated number of clusters: 3
:srcset: /auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 0.167 seconds)
.. _sphx_glr_download_auto_examples_cluster_plot_dbscan.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_dbscan.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/cluster/plot_dbscan.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_dbscan.ipynb <plot_dbscan.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_dbscan.py <plot_dbscan.py>`
.. include:: plot_dbscan.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_