title: Kmeans based on two initial given clusters
body: <p>I have data points represented on a 2D graph which is shown as below.</p>
  
  <pre><code>1,1
  1,2
  2,1
  2,2
  3,3
  8,8
  9,8
  8,9
  9,9
  </code></pre>
  
  <p>EDIT:
  <code>Now, if I have two initial given clusters, let's say (1,1) and (2,1), I want to use K means to decide the clusters based on Euclidean Distances with termination condition of 3 iterations.</code></p>
  
  <p>My approach was to create a pandas dataframe, apply kmeans with 2 clusters and note the centroids. </p>
  
  <pre><code>import pandas as pd
  df = pd.DataFrame({'x': [1,1,2,2,3,8,9,8,9], 
                     'y': [1,2,1,23,8,8,9,9]})
  from sklearn.cluster import KMeans
  kmeans = KMeans(2).fit(df.values)
  </code></pre>
  
  <p>But, I am stuck. How can i define my own customise function for the same.</p>
  
  <p>I want to iterate it to three next iterations and note the calculations to reach the exact clusters in those three iterations.</p>
  
  <p>Thanks</p>