diff --git a/examples/cluster/plot_dbscan.py b/examples/cluster/plot_dbscan.py
index 85df50c69cb87..74d71b2042b59 100644
--- a/examples/cluster/plot_dbscan.py
+++ b/examples/cluster/plot_dbscan.py
@@ -1,26 +1,26 @@
-# -*- coding: utf-8 -*-
 """
 ===================================
 Demo of DBSCAN clustering algorithm
 ===================================
 
-DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
-finds core samples of high density and expands clusters from them.
-This algorithm is good for data which contains clusters of similar density.
+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.
 
 """
 
-import numpy as np
+# %%
+# Data generation
+# ---------------
+#
+# We use :class:`~sklearn.datasets.make_blobs` to create 3 synthetic clusters.
 
-from sklearn.cluster import DBSCAN
-from sklearn import metrics
 from sklearn.datasets import make_blobs
 from sklearn.preprocessing import StandardScaler
 
-
-# %%
-# Generate sample data
-# --------------------
 centers = [[1, 1], [-1, -1], [1, -1]]
 X, labels_true = make_blobs(
     n_samples=750, centers=centers, cluster_std=0.4, random_state=0
@@ -28,12 +28,26 @@
 
 X = StandardScaler().fit_transform(X)
 
+# %%
+# We can visualize the resulting data:
+
+import matplotlib.pyplot as plt
+
+plt.scatter(X[:, 0], X[:, 1])
+plt.show()
+
 # %%
 # 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`.
+
+import numpy as np
+from sklearn.cluster import DBSCAN
+from sklearn import metrics
+
 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.
@@ -42,23 +56,46 @@
 
 print("Estimated number of clusters: %d" % n_clusters_)
 print("Estimated number of noise points: %d" % n_noise_)
-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))
+
+# %%
+# 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.
+
+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: %0.3f"
-    % metrics.adjusted_mutual_info_score(labels_true, labels)
+    "Adjusted Mutual Information:"
+    f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}"
 )
-print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))
+print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}")
 
 # %%
-# Plot result
-# -----------
-import matplotlib.pyplot as plt
+# Plot results
+# ------------
+#
+# Core samples (large dots) and non-core samples (small dots) are color-coded
+# according to the asigned cluster. Samples tagged as noise are represented in
+# black.
 
-# Black removed and is used for noise instead.
 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:
@@ -87,5 +124,5 @@
         markersize=6,
     )
 
-plt.title("Estimated number of clusters: %d" % n_clusters_)
+plt.title(f"Estimated number of clusters: {n_clusters_}")
 plt.show()