centroid.java

KMeans Algrorithm for search engine

import java.io.*;
import java.util.*;
import java.lang.*;
import java.text.*;
 
public class Centroid {
 
   public void Grouping(double[] Cordx, double[] Cordy, int clustNumber) {
      int clusterNumber = clustNumber;
      double[] ClustCordX = new double[clustNumber];
      double[] ClustCordY = new double[clustNumber];
      this.getMeansetCentroid(Cordx, Cordy,  clustNumber);//finds the means of the data set and set controls
      DecimalFormat dec = new DecimalFormat("0.00");
      for(int i = 0;i<Cordx.length;i++) {
         String result1 = dec.format(Cordx[i]);
         String result2 = dec.format(Cordy[i]);
         System.out.println("Cords are ( " + result1 + " , " + result2 + ")");
      }
 
      for(int i = 0; i<clustNumber;i++) { //Setting the Number of Clusters by randomly selecting Centroids;
         ClustCordX[i] = Cordx[i];
         ClustCordY[i] = Cordy[i];
      }
 
 
      this.groupCordtoCluster(Cordx,Cordy,ClustCordX,ClustCordY);
   }
 
   public void groupCordtoCluster(double[] Cordx, double[] Cordy, double[] ClustCordX, double[] ClustCordY) {
      double temp ;
      int size = Cordx.length;
      int clustsize = ClustCordX.length;
      int clusterComparison = clustsize;
      int[] grouping = new int[size - clustsize];
      double[] ClustgroupX = new double[size - clustsize];
      double[] ClustgroupY = new double[size - clustsize];
      int tempint = -1;
/*Groups the data into clusters, then returns an array of points
for which group(centroid) it belongs to*/
 
      for(int i = clusterComparison; i < size;i++) {
          temp = 0;
         for(int j = 0;j<clustsize;j++) {
            if (j == 0)
               tempint++;
            if(temp == 0) {
               temp = Math.sqrt(Math.pow((Cordx[i]-ClustCordX[j]),2) + Math.pow((Cordy[i]-ClustCordY[j]),2));
               grouping[tempint] = j;
               ClustgroupX[tempint] = Cordx[i];
               ClustgroupY[tempint] = Cordy[i];
            }
            else if (temp > Math.sqrt(Math.pow((Cordx[i]-ClustCordX[j]),2) + Math.pow((Cordy[i]-ClustCordY[j]),2))) {        
               temp = Math.sqrt(Math.pow((Cordx[i]-ClustCordX[j]),2) + Math.pow((Cordy[i]-ClustCordY[j]),2));
               grouping[tempint] = j; 
               ClustgroupX[tempint] = Cordx[i];
               ClustgroupY[tempint] = Cordy[i];
            }
         }
 
 
      }
      DecimalFormat dec = new DecimalFormat("0.00");
      String result1, result2, result3, result4;
      for(int i = 0; i<grouping.length;i++) {
         System.out.println("------------------------");
         System.out.println("Clusters for group " + grouping[i]);
         result1 = dec.format(Cordx[grouping[i]]);
         result2 = dec.format(Cordy[grouping[i]]);
         result3 = dec.format(ClustgroupX[i]);
         result4 = dec.format(ClustgroupY[i]);
         System.out.println("Cordinates are (" + result1 + " , " + result2 + ")");
         System.out.println("------------------------");
         System.out.println("Clusters for group " + grouping[i]);
         System.out.println("Cordinates are (" + result3 + " , " + result4 + ")");
 
      }
   }
   
   public void getMeansetCentroid(double[] Cordx, double[] Cordy, int ClustNumber) {
      double xCord, yCord;
      double MAX=0,distance, tempd1, tempd2;
      double[] Distances = new double[Cordx.length];
      int reference, i, j, temp1, temp2, point, length;
      reference = i = j = temp1 = temp2 = point =0;
      int[] centroids;
/*This part of the function finds the distances with reference
with the first data set. It then reorders the data from minimum distance
to maximum distance in relation to the very first point.*/
 
      for(j = 1; j < Cordx.length;j++) {
         Distances[j-1] = Math.sqrt(Math.pow((Cordx[j]-Cordx[0]),2) + Math.pow((Cordy[j]-Cordy[0]),2));
      }
      for(i=0;i<Cordx.length-1;i++) {
         for(j=0;j<Cordx.length-1-i;j++) {
            if(Distances[j+1] < Distances[j]) {
               distance = Distances[j];
               tempd1 = Cordx[j];
               tempd2 = Cordy[j];
               Distances[j] = Distances[j+1];
               Cordx[j] = Cordx[j+1];
               Cordy[j] = Cordy[j+1];
               Distances[j+1] = distance;
               Cordx[j+1] = tempd1;
               Cordy[j+1] = tempd2;
            }
         }
      }
/*Then reorganizes the data sets by placing mean
of the centroid reference points at the front
to allow another function to cluster the data's together*/
 
      point = Cordx.length;
      do {
         if(Cordx.length % ClustNumber != 0)
            point--;
      }while(point % ClustNumber != 0);
      length = point/ClustNumber;
      for(i=0;i<Cordx.length;i=length+i) {
         if((i+length-1) > point)
           break; 
         tempd1 = Cordx[i];
         tempd2 = Cordy[i];
         Cordx[i] = Cordx[i+length-1];
         Cordy[i] = Cordy[i+length-1];
         Cordx[i+length-1] = tempd1;
         Cordy[i+length-1] = tempd2;
      }
 
   }
 
   public double genRandom(int Data) {
      double randomValue = Math.random() * 100;
      return randomValue;
   }
 
}