title: unstable result from scipy.cluster.kmeans
body: <p>The following code gives different results at every runtime while clustering the data into 3 parts using the k means method: </p>
  
  <pre><code>from numpy import array
  from scipy.cluster.vq import kmeans,vq
  
  data = array([1,1,1,1,1,1,3,3,3,3,3,3,7,7,7,7,7,7])
  centroids = kmeans(data,3,100) #with 100 iterations
  print (centroids)
  </code></pre>
  
  <p>Three possible results obtained were:</p>
  
  <pre><code>(array([1, 3, 7]), 0.0)
  (array([3, 7, 1]), 0.0)
  (array([7, 3, 1]), 0.0)
  </code></pre>
  
  <p>Actually, the order of the calculated k means are different. But, does not it unstable to assign which k means point belongs to which cluster? Any idea??</p>