title: DBSCAN sklearn is very slow
body: <p>I am trying to cluster a dataset has more than 1 million data points. One column has text and the other column has a numeric value corresponding to it. The problem that I am facing is that it gets stuck and never completes. I have tried to work with smaller datasets of around 100,000 and it works fairly quickly but as I start increasing data points it starts slowing down and for a million it never completes and hangs. Initially, I thought it might be because I have a tfidf matrix for text and there are 100 dimensions so it is taking a long time. Then I tried clustering based on the amount which is just a single value for each data point and it still did not complete. Below is the code snippet. Any idea what I might be doing wrong? I have seen people working with larger data sets and having no problem.</p>
  
  <pre><code>Y=data['amount'].values
  Y=Y.reshape(-1,1)
  dbscan = DBSCAN(eps=0.3, min_samples= 10, algorithm='kd_tree')
  dbscan.fit_predict(Y)
  labels = dbscan.labels_
  print(labels.size)
  clusters = labels.tolist()
  #printing the value and its label
  for a, b in zip(labels, Y):
      print(a, b)
  </code></pre>