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    <p>You need to count the words in each document and make a feature generally called bag of words. Before that you need to remove stop words(very common but not giving much information like the, a etc). You can generally take top n common words from your document. Count the frequency of these words and store them in n dimensional vector.</p> <p>For distance measure you can use cosine vector.</p> <p>Here is a simple algorithm for 2 mean for 1 dimensional data points. you can extend it to k mean and n dimensional data point easily. Let me know if you want n dim implementation.</p> <p><pre><code> double[] x = {1,2,2.5,3,3.5,4,4.5,5,7,8,8.5,9,9.5,10};</p> <p>double[] center = new int[2]; double[] precenter = new int[2]; ArrayList[] cluster = new ArrayList[2];</p> <p>//generate 2 random number from 0 to x.length without replacement int rand = new int[2]; Random rand = new Random(); rand[0] = rand.nextInt(x.length + 1); rand[1] = rand.nextInt(x.length + 1);</p> <p>while(rand[0] == rand[1] ){ rand[1] = rand.nextInt(x.length + 1); } center[0] = x[rand[0]]; center[1] = x[rand[1]]; //there is a better way to generate k random number (w/o replacement) just search.</p> <p>do{ cluster[0].clear(); cluster[1].clear(); for(int i = 0; i &lt; x.length; ++i){ if(abs(x[i]-center1[0]) &lt;= abs(x[i]-center1[1])){ cluster[0].add(x[i]); } else{ cluster[0].add(x[i]); } precenter[0] = center[0]; precenter[1] = center[1];<br> center[0] = mean(cluster[0]); center[1] = mean(cluster[1]); } } while(precenter[0] != center[0] &amp;&amp; precenter[1] != center[1]);</p> <p>double mean(ArrayList list){ double mean = 0; double sum = 0; for(int index=0;index }</p> <p></pre></code> The cluster[0] and cluster [1] contain points in the clusters and center[0], center[1] are the 2 means. you need to do some debugging because I have written the code in R and just converted it into java for you :)</p>
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    1. POSteps to perform document clustering using k-means algorithm in java
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    1. COHi Atul, Thanks for your reply. My work till now : Removed stop words Frequency count for each word in whole documents Then how to perform the cluster using k-means.Please suggest ideas
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    2. COIn your case each point would be an n-dimensional vectors of word frequency and the Euclid distance would be replaced by cosine similarity. These are too much to implement. You can have a look at Manhout. Here is the link https://cwiki.apache.org/confluence/display/MAHOUT/K-Means+Clustering It has simple java classes to implement k-mean clustering with examples. You can run it on your machine and the same code would run on hadoop cluster also. Some code using manhout k mean on netflix data here: http://code.google.com/p/canopy-clustering/
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