title: Document Clustering in python using SciKit
body: <p>I recently started working on Document clustering using SciKit module in python. However I am having a hard time understanding the basics of document clustering. </p>
  
  <p>What I know ?</p>
  
  <ul>
  <li>Document clustering is typically done using TF/IDF. Which essentially
  converts the words in the documents to vector space model which is
  then input to the algorithm.</li>
  <li>There are many algorithms like k-means, neural networks, hierarchical
  clustering to accomplish this.</li>
  </ul>
  
  <p>My Data :</p>
  
  <ul>
  <li>I am experimenting with linkedin data, each document would be the
  linkedin profile summary, I would like to see if similar job
  documents get clustered together.</li>
  </ul>
  
  <p>Current Challenges: </p>
  
  <ul>
  <li>My data has huge summary descriptions, which end up becoming 10000's
  of words when I apply TF/IDF. Is there any proper way to handle this
  high dimensional data.</li>
  <li>K - means and other algorithms requires I specify the no. of clusters
  ( centroids ), in my case I do not know the number of clusters
  upfront. This I believe is a completely unsupervised learning. Are
  there algorithms which can determine the no. of clusters themselves?</li>
  <li>I've never worked with document clustering before, if you are aware
  of tutorials , textbooks or articles which address this issue, please
  feel free to suggest.</li>
  </ul>
  
  <p>I went through the code on SciKit webpage, it consists of too many technical words which I donot understand, if you guys have any code with good explanation or comments please share. Thanks in advance. </p>