Machine-learning-Algorithm-codes/KMeans.java at main · roshnithakor07/Machine-learning-Algorithm-codes · GitHub

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// package kmeans;

public class KMeans
{

	public static void main(String[] args) {
		int data[]={2,4,-10,12,3,20,30,11};//,25,17,23};    // initial data
		int noofclusters=3;
		int centroid[][]=new int[][]{
			{0,0,0},
			{2,4,30}
		};
		getCentroid(data,noofclusters,centroid);

	}

	public static int[][] getCentroid(int data[],int noofclusters,int centroid[][]){

		int distance[][]=new int[noofclusters][data.length];
		int cluster[]=new int[data.length];
		int clusternodecount[]=new int[noofclusters];

		centroid[0]=centroid[1];
		centroid[1]=new int[]{0,0,0};
		System.out.println("========== Starting to get new centroid =========");

		for(int i=0;i<noofclusters;i++){
			for(int j=0;j<data.length;j++){
				//System.out.println(distance[i][j]+"("+i+","+j+")="+data[j]+"("+j+")-"+centroid[0][i]+"="+(data[j]-centroid[0][i]));
				distance[i][j]=Math.abs(data[j]-centroid[0][i]);
				System.out.print(distance[i][j]+" ,");
				//System.out.println("Centroid: "+centroid[0][i]);
			}
			System.out.println();
		}

		for(int j=0;j<data.length;j++){
			int smallerDistance=0;
			if(distance[0][j]<distance[1][j] && distance[0][j]<distance[2][j])
				smallerDistance=0;
			if(distance[1][j]<distance[0][j] && distance[1][j]<distance[2][j])
				smallerDistance=1;
			if(distance[2][j]<distance[0][j] && distance[2][j]<distance[1][j])
				smallerDistance=2;//

			centroid[1][smallerDistance]=centroid[1][smallerDistance]+data[j];
			clusternodecount[smallerDistance]=clusternodecount[smallerDistance]+1;
			cluster[j]=smallerDistance;

			//System.out.println("Centerid at 1:  "+centroid[1][smallerDistance]);
			//System.out.print(cluster[j]+", ");


		}
		//for(int j=0;j<data.length;j++)
		//System.out.println("c at out: "+cluster[j]);

                System.out.println("======================================== ");

                System.out.println("New clusters are ");
              	// cluster[]= { 0  1   0  1  0  2  2  1}
                // data[]={2,4,-10,12,3,20,30,11};
                 for(int i=0;i<noofclusters;i++){
			        System.out.print("C"+(i+1)+": ");
                     for(int l=0;l<data.length;l++){
					if(cluster[l]==i)
						System.out.print(data[l]+" ,");

				}
				System.out.println();
			}
                System.out.println("======================================== ");

		System.out.println("New centroid is ");

		for(int j=0;j<noofclusters;j++){
			centroid[1][j]=centroid[1][j]/clusternodecount[j];
			System.out.print(centroid[1][j]+",");
		}
		System.out.println();

		boolean isAchived=true;
		for(int j=0;j<noofclusters;j++){
			if(isAchived && centroid[0][j] == centroid[1][j]){
				isAchived=true;
				continue;
			}
			isAchived=false;
		}

		if(!isAchived){

			getCentroid(data,noofclusters,centroid);
		}

		if(isAchived){
			System.out.println("======================================== ");
			System.out.println(" Final Cluster is ");
			for(int i=0;i<noofclusters;i++){
                              System.out.print("C"+(i+1)+":");
				for(int j=0;j<data.length;j++){
					if(cluster[j]==i)
						System.out.print(data[j]+" ,");

				}
				System.out.println();
			}
		}

		return centroid;

	}
}