title: How do i visualize data points of tf-idf vectors for kmeans clustering?
body: <p>I have a list of documents and the tf-idf score for each unique word in the entire corpus. 
  How do I visualize that on a 2-d plot to give me a gauge of how many clusters I will need to run k-means?</p>
  
  <p>Here is my code:</p>
  
  <pre><code>sentence_list=["Hi how are you", "Good morning" ...]
  vectorizer=TfidfVectorizer(min_df=1, stop_words='english', decode_error='ignore')
  vectorized=vectorizer.fit_transform(sentence_list)
  num_samples, num_features=vectorized.shape
  print "num_samples:  %d, num_features: %d" %(num_samples,num_features)
  num_clusters=10
  </code></pre>
  
  <p>As you can see, I am able to transform my sentences into a tf-idf document matrix. But I am unsure how to plot the data points of the tf-idf score.</p>
  
  <p>I was thinking:</p>
  
  <ol>
  <li>Add more variables like document length and something else</li>
  <li>do PCA to get an output of 2 dimensions</li>
  </ol>
  
  <p>Thanks</p>