title: Understanding "score" returned by scikit-learn KMeans
body: <p>I applied clustering on a set of text documents (about 100). I converted them to <code>Tfidf</code> vectors using <code>TfIdfVectorizer</code> and supplied the vectors as input to <code>scikitlearn.cluster.KMeans(n_clusters=2, init='k-means++', max_iter=100, n_init=10)</code>. Now when I</p>
  
  <pre><code>model.fit()
  print model.score()
  </code></pre>
  
  <p>on my vectors, I get a very small value if all the text documents are very similar, and I get a very large negative value if the documents are very different.</p>
  
  <p>It serves my basic purpose of finding which set of documents are similar, but can someone help me understand what exactly does this <code>model.score()</code> value signify for a fit? How can I use this value to justify my findings?</p>