Java Implementation of Kmeans Algorithm on 2D Dataset

Kmean2D.java

/** The class encapsulates an implementation of the Kmeans algorithm for 2D Dataset
 *
 * First	argument is for Number of Elements
 * Second	argument is for Start of Randomizer
 * Third	argument is for End of Randomizer
 * Fourth	argument is for Number of Clusters
 *
 * Usage with the command line :
 * >java Kmean2D 7 10 20 3
 * This Means 7 tupples of data will be created
 * With each tupple having randomized values between 10 to 40
 * And 3 clusters will be formed.
 *
 * As per Kmeans Algorithm when the datasets from previous iteration belong to the same clusters in current iteration
 * The Algorithm stops and clusters are finalized
 * 
 * @author Tomrock D'souza, St. Francis Institute Of Technology, University of Mumbai, 2017
 * @copyright GNU General Public License v3
 * No reproduction in whole or part without maintaining this copyright notice
 * and imposing this condition on any subsequent users.
 */
import java.util.* ;
import java.lang.Math;
class Kmean2D {
	
	//Euclidean's Distance Formula
	public static double Dist(Double a, Double b, Double c, Double d) {
		return Math.sqrt(Math.pow((a - c), 2) + Math.pow((b - d), 2));
	}
	
	public static void main(String args[]) {
		int number,numSize,numStart,numEnd,x,y = 0,cSize,l,k,z,i,j;
		Double temp,tempa;
		//Processing Arguments
		numSize = Integer.parseInt(args[0]);
		numStart = Integer.parseInt(args[1]);
		numEnd = Integer.parseInt(args[2]);
		cSize = Integer.parseInt(args[3]);
		x = numEnd - numStart + 1;
		Integer[] rand = new Integer[x];
		for (z = numStart; z <= numEnd; z++) {
			rand[y] = z;
			y++;
		}
		//Filling Array 1 With Random Variables
		Collections.shuffle(Arrays.asList(rand));
		Integer[] arrayX = Arrays.copyOfRange(rand, 0, numSize + 1);
		//Filling Array 2 With Random Variables
		Collections.shuffle(Arrays.asList(rand));
		Integer[] arrayY = Arrays.copyOfRange(rand, 0, numSize + 1);
		
		
		Integer[] clustvalue = new Integer[numSize];
		Integer[] clusttemp = new Integer[numSize];
		
		Double[] clustX = new Double[cSize];
		Double[] clustY = new Double[cSize];
		
		//Presigning Clusters And Getting Numerical Ready For First Iteration
		y = numSize / cSize;
		z = 0;
		x = 0;
		for (i = 0; i < cSize - 1; i++) {
			l = 0;
			for (j = 0; j < y; j++) {
				clustvalue[z] = i;
				z++;
			}
		}

		for (; z < numSize; z++) {
			clustvalue[z] = cSize - 1;
		}

		System.out.println("arrayX\tarrayY\tAssigned-cluster");
		for (i = 0; i < numSize; i++) {
			System.out.println(arrayX[i] + "\t" + arrayY[i] + "\t" + clustvalue[i]);
		}
		//At this stage The first assignment of clusters to the data sets is done/
		
		
		
		while (true) {
			//Get Cluster Value
			for (i = 0; i < cSize; i++) {
				l = 0;
				k = 0;
				for (j = 0; j < numSize; j++) {

					if (clustvalue[j] == i) {
						l += arrayX[j];
						k += arrayY[j];
						x++;
					}
				}
				clustX[i] = (double) l / x;
				clustY[i] = (double) k / x;
				x = 0;
			}
			
			//Find Distance of dataset with each Cluster and select the lowest
			for (i = 0; i < numSize; i++) {
				temp = 9999.9;
				for (j = 0; j < cSize; j++) {
					tempa = Dist(clustX[j], clustY[j], (double) arrayX[i], (double) arrayY[i]);
					if (temp > tempa) {
						clusttemp[i] = j;
						temp = tempa;
					}
				}
			}
			
			//Printing out data for Iteration
			for (i = 0; i < cSize; i++) {
				System.out.println("Current Cluster Value " + i + " =(" + clustX[i] + "," + clustY[i] + ")");
			}
			System.out.println("\n\narrayX\t\tarrayY\t\tPre-clus\tCurr-Clus");
			for (i = 0; i < numSize; i++) {
				System.out.println(arrayX[i] + "\t\t" + arrayY[i] + "\t\t" + clustvalue[i] + "\t\t" + clusttemp[i]);
			}

			
			//Condition that matches current and previous data set
			if (!Arrays.equals(clusttemp, clustvalue)) {
				clustvalue = clusttemp.clone();
			}
			else {
				for (i = 0; i < cSize; i++) {
					l = 0;
					k = 0;
					for (j = 0; j < numSize; j++) {

						if (clustvalue[j] == i) {
							l += arrayX[j];
							k += arrayY[j];
							x++;
						}
					}
					clustX[i] = (double) l / x;
					clustY[i] = (double) k / x;
					x = 0;
				}
				System.out.println("\nFinal Cluster Values:");
				for (i = 0; i < cSize; i++) {
					System.out.println("Cluster Value " + i + " =(" + clustX[i] + "," + clustY[i] + ")");
				}
				break;}

		}
	}

}