title: How does PCA gives centers for the Kmeans algorithm in scikit learn
body: <p>I'm looking at this example code given on <a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html" rel="nofollow">Scikit Kmeans digit example</a></p>
  
  <p>There is the following code in this script :</p>
  
  <pre><code># in this case the seeding of the centers is deterministic, hence we run the
  # kmeans algorithm only once with n_init=1
  pca = PCA(n_components=n_digits).fit(data)
  bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
            name="PCA-based",
            data=data)
  </code></pre>
  
  <p>Why are the eigen vectors used as initial centers and is there any intuition for this?</p>