K-means Clustering

 What is K-means? 
1. Partitional clustering approach 
2. Each cluster is associated with a centroid (center point) 
3. Each point is assigned to the cluster with the closest centroid 
4 Number of clusters K must be specified 


Following Code will find the value of K :


Code:

Main.Java

import java.util.ArrayList;

import java.util.Collections;

public class Main {

   public static void main(String[] args) {
        long start=System.currentTimeMillis();
       int k=1;
      Point[] p=new Point[5];
      p[0]=new Point(1,1);
      p[1]=new Point(1,0);
      p[2]=new Point(0,2);
      p[3]=new Point(2,4);
      p[4]=new Point(3,5);
      
      ArrayList<Point> given=new ArrayList<>();
      given.add(p[0]);
      given.add(p[1]);
      given.add(p[2]);
      given.add(p[3]);
      given.add(p[4]);
      
      Collections.shuffle(given);
      Point c1=new Point(given.get(0));
      Point c2=new Point(given.get(1));


      ArrayList<Point>cluster1=new ArrayList<>();
      ArrayList<Point>cluster2=new ArrayList<>();

      ArrayList<Point>newcluster1=new ArrayList<>();
      ArrayList<Point>newcluster2=new ArrayList<>();

      double dc1,dc2;
      for(int i=0;i<5;i++) {
         dc1=c1.distance(p[i]);
         dc2=c2.distance(p[i]);
         if(dc1<dc2) {
            cluster1.add(p[i]);
         }else if(dc2<dc1) {
            cluster2.add(p[i]);
         }
      }
        c1.x=meanx(cluster1);
        c1.y=meany(cluster1);
        c2.x=meanx(cluster2);
        c2.y=meany(cluster2);
        do {
            if(!(newcluster1.isEmpty()&& newcluster2.isEmpty())) {
                cluster1.clear();
                System.out.println(cluster1.size());
                cluster1.addAll(newcluster1);
                cluster2.clear();
                cluster2.addAll(newcluster2);
                newcluster1.clear();
                newcluster2.clear();
            }
            for(int i=0;i<5;i++) {
                dc1=c1.distance(p[i]);
                dc2=c2.distance(p[i]);
                if(dc1<=dc2) {
                    newcluster1.add(p[i]);
                }else {
                    newcluster2.add(p[i]);
                }
            }
            c1.x=meanx(newcluster1);
            c1.y=meany(newcluster1);
            c2.x=meanx(newcluster2);
            c2.y=meany(newcluster2);
            k++;
        }while(!(compareCluster(cluster1, newcluster1)||compareCluster(cluster1, newcluster2))&&(compareCluster(cluster2, newcluster2)||compareCluster(cluster2, newcluster1)));
        System.out.println("Value of K is  : "+k);
      System.out.println("Time Taken : "+(System.currentTimeMillis()-start)+" milliseconds");
   }

    static boolean compareCluster(ArrayList<Point> c1,ArrayList<Point> c2) {
      if(c1.size()!=c2.size()||c1==null&&c2!=null||c1!=null&&c2==null) return false;
      if(c1==null && c2==null) return true;
      for(int i=0;i< c1.size();i++){
           if(! c2.contains(c1.get(i))) return false;
        }
        return true;
   }


   static double meanx(ArrayList<Point> c) {
      double sumx=0;
      for(int i=0;i<c.size();i++) {
         sumx+=c.get(i).x;
      }
      return sumx/c.size();
   }

    static double meany(ArrayList<Point> c) {
        double sumy=0;
        for(int i=0;i<c.size();i++) {
            sumy+=c.get(i).y;
        }
        return sumy/c.size();
    }
}

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Point.Java

public class Point {
   double x,y;
   
   Point(double a,double b){
      x=a;
      y=b;
   }
   Point(Point b){
      x=b.x;
      y=b.y;
   }
   
   double distance(Point b) {
      return Math.sqrt(Math.pow((x-b.x),2)+Math.pow((y-b.y),2));
   }

   @Override   public String toString() {
      return "Point [x=" + x + ", y=" + y + "]";
   }

}




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Output: