title: Using k-means clustering on web log data
body: <p>I have a data set from a access web log file which I'm interested in finding similar clusters. (I'm an absolute beginner of data mining). So far I have referred many research papers on the same problem domain.</p>
  
  <p><a href="http://www.sersc.org/journals/IJAST/vol5/1.pdf" rel="nofollow">An Efficient Approach for Clustering Web Access Patterns from
  Web Logs</a> </p>
  
  <p><a href="http://faculty.ist.psu.edu/jjansen/academic/jansen_user_intent_kmeans.pdf" rel="nofollow">Classifying the user intent of web queries using k-means clustering</a></p>
  
  <p>I want to use k-means clustering to cluster web pages. Although these papers discuss about the algorithm, they do not specify the way of providing input data set. <code>k-means</code> calculate similarity between data points using Euclidean distance. So how to normalize my datset to be mined using <code>k-means</code> since urls can not directly used for k-means. Any help/good reference on this?</p>
  
  <p>Example Dataset(p1..pn are different web pages)</p>
  
  <pre><code>p1,p2,p3,p4
  p1,p2
  p1,p5,p6,p7
  p1,p2,p3,p5
  </code></pre>