
  K-Means Clustering Comments 

  1. Cluster a data file using K-Means Clustering, the initial cluster
     centers given by Sequential Leader algorithm. 
  2. Plots a 2-D projection of the clusters and the clustering error 
     versus iteration number. 
  

 Input details

  1.Choose the training file from the training data folder.
  2.Choose the total number of columns in the training file.
  3.Choose the number of columns to be classified(number of inputs).
  4.Choose the number of clusters.
  5.Choose the number of iterations.

 Example:


  TWOD.TRA ! training file
  15       ! totoal number of columns
  8	   ! number of columns to be classified 
  20	   ! number of clusters
  20	   ! number of iterations


     we see that the program will apply K-Means clustering algorithm
     to the file Twod.tra. Then 20 iterations of K-Means clustering will be used.
     The clusters will be saved in a file. After running the program, we can 
     observe that the normalized clustering error is 4.905475.

     You can run this program on your own data.

     

