title: Scikit DBSCAN eps and min_sample value determination
body: <p>I have been trying to implement DBSCAN using scikit and am so far failing to determine the values of epsilon and min_sample which will give me a sizeable number of clusters. I tried finding the average value in the distance matrix and used values on either side of the mean but haven't got a satisfactory number of clusters:</p>
  
  <p>Input:</p>
  
  <pre><code>db=DBSCAN(eps=13.0,min_samples=100).fit(X)
  labels = db.labels_
  
  # Number of clusters in labels, ignoring noise if present.
  n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
  print('Estimated number of clusters: %d' % n_clusters_)
  </code></pre>
  
  <p>output: </p>
  
  <pre><code>Estimated number of clusters: 1
  </code></pre>
  
  <p>Input:</p>
  
  <pre><code>db=DBSCAN(eps=27.0,min_samples=100).fit(X)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>Estimated number of clusters: 1
  </code></pre>
  
  <p>Also so other information:</p>
  
  <pre><code>The average distance between any 2 points in the distance matrix is 16.8354
  the min distance is 1.0
  the max distance is 258.653
  </code></pre>
  
  <p>Also the X passed in the code is not the distance matrix but the matrix of feature vectors.
  So please tell me how do i determine these parameters</p>