K-means clustering on WebLog Data, for 2-Dimenshional input

The following is a JSP code for K-MEANS CLUSTERING .... My proect , titled "Pattern Analysis of Web Usage for System Improvement", involves the web log info from a database and then considering the attributes IP address and WEBPAGES,IN THE FORM OF NUMBERS(X,Y) for K-MEANS CLUSTERING... the output of this is used for WEB CACHE REPLACEMENT.. please let me knw,if the following code of K-MEANS CLUSTERING in JSP is proper and will take 2 dimenshional input(ip,webpages)... i m having hard time in converting ip and webpages into number for K-MEANS CLUSTERING...please reply.

<html>
<head>
<script type="text/javascript">

function kmeans( arrayToProcess, Clusters )
{

  var Groups = new Array();
  var Centroids = new Array();
  var oldCentroids = new Array();
  var changed = false;

  // order the input array
  arrayToProcess.sort(function(a,b){return a - b})  

  // initialise group arrays
  for( initGroups=0; initGroups < Clusters; initGroups++ )
  {

    Groups[initGroups] = new Array();

  }  

  // pick initial centroids

  initialCentroids=Math.round( arrayToProcess.length/(Clusters+1) );  

  for( i=0; i<Clusters; i++ )
  {

    Centroids[i]=arrayToProcess[ (initialCentroids*(i+1)) ];

  }

  do
  {

    for( j=0; j<Clusters; j++ )
    {

      Groups[j] = [];

    }

    changed=false;

    for( i=0; i<arrayToProcess.length; i++ )
    {

      Distance=-1;
      oldDistance=-1

      for( j=0; j<Clusters; j++ )
      {

        distance = Math.abs( Centroids[j]-arrayToProcess[i] );  

        if ( oldDistance==-1 )
        {

           oldDistance = distance;
           newGroup = j;

        }
        else if ( distance <= oldDistance )
        {

            newGroup=j;
            oldDistance = distance;

        }

      } 

      Groups[newGroup].push( arrayToProcess[i] );     

    }

    oldCentroids=Centroids;

    for ( j=0; j<Clusters; j++ )
    {

      total=0;
      newCentroid=0;

      for( i=0; i<Groups[j].length; i++ )
      {

        total+=Groups[j][i];

      } 

      newCentroid=total/Groups[newGroup].length;  

      Centroids[j]=newCentroid;

    }

    for( j=0; j<Clusters; j++ )
    {

      if ( Centroids[j]!=oldCentroids[j] )
      {

        changed=true;

      }

    }

  }
  while( changed==true );

  return Groups;
}


    </script>
</head>
<html>
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