diff --git a/examples/cluster/plot_bisect_kmeans.py b/examples/cluster/plot_bisect_kmeans.py
index 0f107c96e95d1..a6be3545e0b27 100644
--- a/examples/cluster/plot_bisect_kmeans.py
+++ b/examples/cluster/plot_bisect_kmeans.py
@@ -5,10 +5,12 @@
 
 This example shows differences between Regular K-Means algorithm and Bisecting K-Means.
 
-While K-Means clusterings are different when with increasing n_clusters,
-Bisecting K-Means clustering build on top of the previous ones.
-
-This difference can visually be observed.
+While K-Means clusterings are different when increasing n_clusters,
+Bisecting K-Means clustering builds on top of the previous ones. As a result, it
+tends to create clusters that have a more regular large-scale structure. This
+difference can be visually observed: for all numbers of clusters, there is a
+dividing line cutting the overall data cloud in two for BisectingKMeans, which is not
+present for regular K-Means.
 
 """
 import matplotlib.pyplot as plt
@@ -21,13 +23,13 @@
 
 
 # Generate sample data
-n_samples = 1000
+n_samples = 10000
 random_state = 0
 
 X, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)
 
 # Number of cluster centers for KMeans and BisectingKMeans
-n_clusters_list = [2, 3, 4, 5]
+n_clusters_list = [4, 8, 16]
 
 # Algorithms to compare
 clustering_algorithms = {
@@ -37,7 +39,7 @@
 
 # Make subplots for each variant
 fig, axs = plt.subplots(
-    len(clustering_algorithms), len(n_clusters_list), figsize=(15, 5)
+    len(clustering_algorithms), len(n_clusters_list), figsize=(12, 5)
 )
 
 axs = axs.T