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diff --git a/‎examples/cluster/plot_dbscan.py
Lines changed: 10 additions & 15 deletions b/‎examples/cluster/plot_dbscan.py
Lines changed: 10 additions & 15 deletions
diff --git a/‎sklearn/cluster/dbscan_.py
Lines changed: 1 addition & 1 deletion b/‎sklearn/cluster/dbscan_.py
Lines changed: 1 addition & 1 deletion
@@ -10,25 +10,24 @@
 print(__doc__)
 
 import numpy as np
-from scipy.spatial import distance
+
 from sklearn.cluster import DBSCAN
 from sklearn import metrics
 from 
10000
sklearn.datasets.samples_generator 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)
+X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
+                            random_state=0)
 
-##############################################################################
-# Compute similarities
-D = distance.squareform(distance.pdist(X))
-S = 1 - (D / np.max(D))
+X = StandardScaler().fit_transform(X)
 
 ##############################################################################
 # Compute DBSCAN
-db = DBSCAN(eps=0.95, min_samples=10).fit(S)
+db = DBSCAN(eps=0.3, min_samples=10).fit(X)
 core_samples = db.core_sample_indices_
 labels = db.labels_
 
@@ -44,20 +43,16 @@
 print("Adjusted Mutual Information: %0.3f"
       % metrics.adjusted_mutual_info_score(labels_true, labels))
 print("Silhouette Coefficient: %0.3f"
-      % metrics.silhouette_score(D, labels, metric='precomputed'))
+      % metrics.silhouette_score(X, labels))
 
 ##############################################################################
 # Plot result
 import pylab as pl
-from itertools import cycle
-
-pl.close('all')
-pl.figure(1)
-pl.clf()
 
 # Black removed and is used for noise instead.
-colors = cycle('bgrcmybgrcmybgrcmybgrcmy')
-for k, col in zip(set(labels), colors):
+unique_labels = set(labels)
+colors = pl.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
+for k, col in zip(unique_labels, colors):
     if k == -1:
         # Black used for noise.
         col = 'k'
 
@@ -165,7 +165,7 @@ def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
         self.random_state = random_state
 
     def fit(self, X):
-        """Perform DBSCAN clustering from vector array or distance matrix.
+        """Perform DBSCAN clustering from features or distance matrix.
 
         Parameters
         ----------