8000 DOC Linked examples for clustering algorithms in their docstrings (#2… · scikit-learn/scikit-learn@7e861bc · GitHub
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DOC Linked examples for clustering algorithms in their docstrings (#26927) (#30127)
Co-authored-by: Maren Westermann <maren.westermann@gmail.com>
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‎sklearn/cluster/_affinity_propagation.py

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@@ -398,9 +398,6 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
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Notes
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-----
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For an example usage,
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see :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`.
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The algorithmic complexity of affinity propagation is quadratic
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in the number of points.
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@@ -442,6 +439,12 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
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>>> clustering.cluster_centers_
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array([[1, 2],
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[4, 2]])
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For an example usage,
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see :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`.
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For a comparison of Affinity Propagation with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_agglomerative.py

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@@ -925,6 +925,9 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
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AgglomerativeClustering()
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>>> clustering.labels_
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array([1, 1, 1, 0, 0, 0])
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For a comparison of Agglomerative clustering with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_birch.py

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@@ -483,6 +483,9 @@ class Birch(
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Birch(n_clusters=None)
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>>> brc.predict(X)
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array([0, 0, 0, 1, 1, 1])
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For a comparison of the BIRCH clustering algorithm with other clustering algorithms,
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see :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_dbscan.py

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@@ -277,9 +277,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):
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Notes
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-----
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For an example, see
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:ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`.
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This implementation bulk-computes all neighborhood queries, which increases
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the memory complexity to O(n.d) where d is the average number of neighbors,
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while original DBSCAN had memory complexity O(n). It may attract a higher
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array([ 0, 0, 0, 1, 1, -1])
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>>> clustering
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DBSCAN(eps=3, min_samples=2)
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For an example, see
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:ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`.
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For a comparison of DBSCAN with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_hdbscan/hdbscan.py

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@@ -427,10 +427,6 @@ class HDBSCAN(ClusterMixin, BaseEstimator):
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:class:`~sklearn.cluster.DBSCAN`), and be more robust to parameter selection.
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Read more in the :ref:`User Guide <hdbscan>`.
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For an example of how to use HDBSCAN, as well as a comparison to
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:class:`~sklearn.cluster.DBSCAN`, please see the :ref:`plotting demo
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<sphx_glr_auto_examples_cluster_plot_hdbscan.py>`.
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.. versionadded:: 1.3
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Parameters

‎sklearn/cluster/_kmeans.py

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@@ -1873,6 +1873,9 @@ class MiniBatchKMeans(_BaseKMeans):
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[1.06896552, 1. ]])
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>>> kmeans.predict([[0, 0], [4, 4]])
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array([1, 0], dtype=int32)
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For a comparison of Mini-Batch K-Means clustering with other clustering algorithms,
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see :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_mean_shift.py

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@@ -432,6 +432,9 @@ class MeanShift(ClusterMixin, BaseEstimator):
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array([1, 0])
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>>> clustering
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MeanShift(bandwidth=2)
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For a comparison of Mean Shift clustering with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_optics.py

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@@ -234,6 +234,9 @@ class OPTICS(ClusterMixin, BaseEstimator):
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For a more detailed example see
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:ref:`sphx_glr_auto_examples_cluster_plot_optics.py`.
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For a comparison of OPTICS with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/cluster/_spectral.py

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@@ -601,6 +601,9 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
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>>> clustering
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SpectralClustering(assign_labels='discretize', n_clusters=2,
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random_state=0)
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For a comparison of Spectral clustering with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

‎sklearn/mixture/_gaussian_mixture.py

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@@ -693,6 +693,9 @@ class GaussianMixture(BaseMixture):
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[ 1., 2.]])
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>>> gm.predict([[0, 0], [12, 3]])
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array([1, 0])
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For a comparison of Gaussian Mixture with other clustering algorithms, see
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:ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
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"""
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_parameter_constraints: dict = {

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