diff --git a/examples/cluster/plot_agglomerative_clustering_metrics.py b/examples/cluster/plot_agglomerative_clustering_metrics.py
index 38fd3682d48ec..f1a77d442dbe8 100644
--- a/examples/cluster/plot_agglomerative_clustering_metrics.py
+++ b/examples/cluster/plot_agglomerative_clustering_metrics.py
@@ -38,6 +38,7 @@
 # License: BSD 3-Clause or CC-0
 
 import matplotlib.pyplot as plt
+import matplotlib.patheffects as PathEffects
 import numpy as np
 
 from sklearn.cluster import AgglomerativeClustering
@@ -80,18 +81,20 @@ def sqr(x):
 
 labels = ("Waveform 1", "Waveform 2", "Waveform 3")
 
+colors = ["#f7bd01", "#377eb8", "#f781bf"]
+
 # Plot the ground-truth labelling
 plt.figure()
 plt.axes([0, 0, 1, 1])
-for l, c, n in zip(range(n_clusters), "rgb", labels):
-    lines = plt.plot(X[y == l].T, c=c, alpha=0.5)
+for l, color, n in zip(range(n_clusters), colors, labels):
+    lines = plt.plot(X[y == l].T, c=color, alpha=0.5)
     lines[0].set_label(n)
 
 plt.legend(loc="best")
 
 plt.axis("tight")
 plt.axis("off")
-plt.suptitle("Ground truth", size=20)
+plt.suptitle("Ground truth", size=20, y=1)
 
 
 # Plot the distances
@@ -106,19 +109,22 @@ def sqr(x):
     avg_dist /= avg_dist.max()
     for i in range(n_clusters):
         for j in range(n_clusters):
-            plt.text(
+            t = plt.text(
                 i,
                 j,
                 "%5.3f" % avg_dist[i, j],
                 verticalalignment="center",
                 horizontalalignment="center",
             )
+            t.set_path_effects(
+                [PathEffects.withStroke(linewidth=5, foreground="w", alpha=0.5)]
+            )
 
-    plt.imshow(avg_dist, interpolation="nearest", cmap=plt.cm.gnuplot2, vmin=0)
+    plt.imshow(avg_dist, interpolation="nearest", cmap="cividis", vmin=0)
     plt.xticks(range(n_clusters), labels, rotation=45)
     plt.yticks(range(n_clusters), labels)
     plt.colorbar()
-    plt.suptitle("Interclass %s distances" % metric, size=18)
+    plt.suptitle("Interclass %s distances" % metric, size=18, y=1)
     plt.tight_layout()
 
 
@@ -130,11 +136,11 @@ def sqr(x):
     model.fit(X)
     plt.figure()
     plt.axes([0, 0, 1, 1])
-    for l, c in zip(np.arange(model.n_clusters), "rgbk"):
-        plt.plot(X[model.labels_ == l].T, c=c, alpha=0.5)
+    for l, color in zip(np.arange(model.n_clusters), colors):
+        plt.plot(X[model.labels_ == l].T, c=color, alpha=0.5)
     plt.axis("tight")
     plt.axis("off")
-    plt.suptitle("AgglomerativeClustering(metric=%s)" % metric, size=20)
+    plt.suptitle("AgglomerativeClustering(metric=%s)" % metric, size=20, y=1)
 
 
 plt.show()