scikit-learn · marenwestermann · Feb 16, 2025 · Oct 21, 2024 · Oct 21, 2024 · Oct 21, 2024
diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py
@@ -398,9 +398,6 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
 
     Notes
     -----
-    For an example usage,
-    see :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`.
-
     The algorithmic complexity of affinity propagation is quadratic
     in the number of points.
 
@@ -442,6 +439,12 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
     >>> clustering.cluster_centers_
     array([[1, 2],
            [4, 2]])
+
+    For an example usage,
+    see :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`.
+
+    For a comparison of Affinity Propagation with other clustering algorithms, see
+    :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
     """
 
     _parameter_constraints: dict = {

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

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

diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
@@ -277,9 +277,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):
 
     Notes
     -----
-    For an example, see
-    :ref:`sphx_glr_auto_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
@@ -322,6 +319,12 @@ class DBSCAN(ClusterMixin, BaseEstimator):
     array([ 0,  0,  0,  1,  1, -1])
     >>> clustering
     DBSCAN(eps=3, min_samples=2)
+
+    For an example, see
+    :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`.
+
+    For a comparison of DBSCAN with other clustering algorithms, see
+    :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
     """
 
     _parameter_constraints: dict = {

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

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

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

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

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

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