package Clustering;

import java.io.BufferedReader;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;

import weka.clusterers.ClusterEvaluation;
import weka.clusterers.DensityBasedClusterer;
import weka.clusterers.EM;
import weka.clusterers.SimpleKMeans;
import weka.core.Instances;

/**
 * An example class that shows the use of Weka clusterers from Java.
 *
 * @author  FracPete
 */

public class KMeans {


    public KMeans() throws Exception{

    	 BufferedReader reader = new BufferedReader(
                 new FileReader("input.arff"));
Instances data = new Instances(reader);
reader.close();

SimpleKMeans kMeans = new SimpleKMeans();
kMeans.setNumClusters(3);
kMeans.buildClusterer(data);

// print out the cluster centroids
Instances centroids = kMeans.getClusterCentroids();
for (int i = 0; i < centroids.numInstances(); i++) {
  System.out.print("Centroid ");
  System.out.print(i + 1);
 System.out.print(": ");
  System.out.print(centroids.instance(i));
  System.out.println();
}

// get cluster membership for each instance
for (int i = 0; i < data.numInstances(); i++) {
	System.out.print(data.instance(i));
	System.out.print(" is in cluster ");
	System.out.print(kMeans.clusterInstance(data.instance(i)) + 1);
	System.out.println();

}

}



    public static void main(String[] args){
    	try {
			KMeans p = new KMeans();
		} catch (Exception e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}

    }
}