diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py
index 33bbcb77ff09a..677421974bdc0 100644
--- a/sklearn/cluster/_affinity_propagation.py
+++ b/sklearn/cluster/_affinity_propagation.py
@@ -258,8 +258,10 @@ def affinity_propagation(
 
     Notes
     -----
-    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
-    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
+    For an example usage,
+    see :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`.
+    You may also check out,
+    :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`
 
     When the algorithm does not converge, it will still return a arrays of
     ``cluster_center_indices`` and labels if there are any exemplars/clusters,
@@ -396,8 +398,8 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
 
     Notes
     -----
-    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
-    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
+    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.
diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
index 903b6befaddb6..7764bff94582f 100644
--- a/sklearn/cluster/_dbscan.py
+++ b/sklearn/cluster/_dbscan.py
@@ -277,8 +277,8 @@ class DBSCAN(ClusterMixin, BaseEstimator):
 
     Notes
     -----
-    For an example, see :ref:`examples/cluster/plot_dbscan.py
-    <sphx_glr_auto_examples_cluster_plot_dbscan.py>`.
+    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,