title: Sklearn kmeans equivalent of elbow method
body: <p>Let's say I'm examining up to 10 clusters, with scipy I usually generate the 'elbow' plot as follows:</p>
  
  <pre><code>from scipy import cluster
  cluster_array = [cluster.vq.kmeans(my_matrix, i) for i in range(1,10)]
  
  pyplot.plot([var for (cent,var) in cluster_array])
  pyplot.show()
  </code></pre>
  
  <p>I have since became motivated to use sklearn for clustering, however I'm not sure how to create the array needed to plot as in the scipy case. My best guess was:</p>
  
  <pre><code>from sklearn.cluster import KMeans
  
  km = [KMeans(n_clusters=i) for i range(1,10)]
  cluster_array = [km[i].fit(my_matrix)]
  </code></pre>
  
  <p>That unfortunately resulted in an invalid command error. What is the best way sklearn way to go about this?</p>
  
  <p>Thank you</p>