diff --git a/examples/cluster/plot_kmeans_assumptions.py b/examples/cluster/plot_kmeans_assumptions.py
index 94f8ff6c58f52..3bdfae86b4ff7 100644
--- a/examples/cluster/plot_kmeans_assumptions.py
+++ b/examples/cluster/plot_kmeans_assumptions.py
@@ -27,7 +27,7 @@
 X, y = make_blobs(n_samples=n_samples, random_state=random_state)
 
 # Incorrect number of clusters
-y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X)
+y_pred = KMeans(n_clusters=2, n_init="auto", random_state=random_state).fit_predict(X)
 
 plt.subplot(221)
 plt.scatter(X[:, 0], X[:, 1], c=y_pred)
@@ -36,7 +36,9 @@
 # Anisotropicly distributed data
 transformation = [[0.60834549, -0.63667341], [-0.40887718, 0.85253229]]
 X_aniso = np.dot(X, transformation)
-y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso)
+y_pred = KMeans(n_clusters=3, n_init=10, random_state=random_state).fit_predict(
+    X_aniso
+)
 
 plt.subplot(222)
 plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred)
@@ -46,7 +48,9 @@
 X_varied, y_varied = make_blobs(
     n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
 )
-y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)
+y_pred = KMeans(n_clusters=3, n_init="auto", random_state=random_state).fit_predict(
+    X_varied
+)
 
 plt.subplot(223)
 plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)
@@ -54,7 +58,9 @@
 
 # Unevenly sized blobs
 X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))
-y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered)
+y_pred = KMeans(n_clusters=3, n_init=10, random_state=random_state).fit_predict(
+    X_filtered
+)
 
 plt.subplot(224)
 plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)