Since my other thread seems dead & I am in desperate need for help. I have decided to post a new thread. Right now I have a list in my algorithm to store data but I want to move to maps. How do I convert the list known as attributes to a map? How do I store SVTM content in a map? How to do the reading?Please someone explain in layman terms. I am really desperate. This is my code below:

Java Code: 
package t_kmeans;
 
import java.io.*;
import java.util.*;
import java.text.DecimalFormat;
import java.util.List;
 
public class BasicKMeans {
    static private String fname= "palsy.dat";
    static List attributes = null;
    static int maxNum = 0;
    static DecimalFormat d2 = new DecimalFormat("##.####");
    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
 
        // TODO code application logic here
        // Get the data file name
 
        // Read SVMlight format data (*.dat)
        attributes =  readSVMLightFile(fname);
        if(attributes!=null){
            System.out.println("Num of attributes : " + maxNum);
            System.out.println("Num of instances : " + attributes.size());
            Kmeans_cluster(attributes);
        }
    }// end of mains
 
 
      // Implementing K-means
  static void Kmeans_cluster(List attributes)
  {
    // Algorithm
    // 1.Randomly generate k centroid points: C={C1,…,Ck}
    // 2.Partition objects into k nonempty subsets (S1,…,Sk) by assigning each object xito the nearest centroid point by calculating distance metrics d(i,j) between the object xiand the centroid points Cj.
    // 3.Stop if no more new assignment: output C, M(C), (S1,…,Sk)
    // 4.Update k centroid points C1,…,Ck using the k subsets (S1,…,Sk)
    // 5.Go back to Step 2
 
    int k = 2;
    double[][] centroids = new double[k][maxNum];
    double[][] temporycentroids = new double[k][maxNum];
    Vector[] cluster_Array = new Vector[k]; // creating k no of cluster
    boolean flag = false;
 
    // implementation
    if (k > attributes.size())
        System.exit(0);
 
    System.out.println("The Total cluster number: " + k);
 
    //1.Randomly generate k centroid points
 
    centroids = randomCentroids(k);
 
 
        // Step 2 and 3
        // Calculate distance matric between each data point with centroids
    do{
 
         cluster_Array = cluster_Array(centroids,k);
 
        //testing the number of k clusters
        for(int idx = 0; idx < k; idx++)
        {
            System.out.println(cluster_Array[idx].size()+ " record "  + " in Cluster "+ idx );
        } // end of step 2 and 3
 
 
        // searching new k number centroids points
        int attribute_id = 1;
        for (int idx = 0; idx < k; idx++)
        {
            for (int idx1 = 0; idx1 < maxNum; idx1++)
            {
                temporycentroids [idx][idx1] = calculate_centroids(cluster_Array[idx],attribute_id);
                attribute_id++;
            }
            attribute_id = 1;
        }
 
        for (int idx = 0; idx < k; idx++)
        {
            System.out.println("centroid point " + idx);
            for (int idx1 = 0; idx1 < maxNum; idx1++)
            {
                System.out.print(idx1+". ");
                System.out.println(temporycentroids[idx][idx1]);
                System.out.print(" ");
            }
            System.out.println();
        }    // end of step 4
 
        // Step 5
        if (compare_centroids(centroids,temporycentroids,k,maxNum) == true)
        {
            flag = true;
            System.out.println("Centroid points are same.");
            for(int idx = 0; idx < k; idx++)
                System.out.println(cluster_Array[idx].size()+ " record "  + " in Cluster "+ idx );
 
            for (int idx = 0; idx < k; idx++)
            {
                System.out.println("centroid point " + idx);
                for (int idx1 = 0; idx1 < maxNum; idx1++)
                {
                    System.out.print(idx1+". ");
                    System.out.println(temporycentroids[idx][idx1]);
                    System.out.print(" ");
 
                }
                System.out.println();
            }
        }
        else    // update centroids
        {
                System.out.println("centroid points are different.");
                centroids = temporycentroids;
                    for (int idx = 0; idx < k; idx++)
                    {
                        System.out.println("updated centroid point " + idx);
                        for (int idx1 = 0; idx1 < maxNum; idx1++)
                        {
                            System.out.print(idx1+". ");
                            System.out.println(centroids[idx][idx1]);
                            System.out.print(" ");
 
                        }
                        System.out.println();
                    }
        }
}while(flag == false);
  }
	// KmeanCluster
 
 
 
 
	// calculate_centroids start
 
  static double calculate_centroids(List attributes, int attribute_id){
     int N = attributes.size();
     double centroid = 0.00;
     if(N > 0)
     {
          for(int idx = 0; idx < N; idx++)
          centroid = centroid + Double.valueOf(d2.format(getAttributeValue(attributes,idx,attribute_id)));
          centroid = Double.valueOf(d2.format(centroid / N));
      }
       return centroid;
  }  // end of calculate_centroids function
 
 
	// start randomCentroids
   static double[][] randomCentroids(int k){
        int[] all = new int[k];
        for (int idx=0; idx<k; idx++){
            all[idx] = 0;  // initialization
        }
        Random rd = new Random();
        int i = 0;
        while (i < k){
            int selectedNumber = rd.nextInt(attributes.size());
            if (adding(all,i,i+1))
                i++;
        }
 
        System.out.println("The data point which is done random : " );
        for( int idx1=0; idx1<k ; idx1++)
            System.out.println(all[idx1]+1 + " ");
 
 
         double [][] temp = new double [k][maxNum];
 
         for(int row = 0;  row< k; row++)
         {   int attrib_id = 1;
             for(int col = 0; col < maxNum; col++)
             {
                 temp[row][col] = Double.valueOf(d2.format(getAttributeValue(attributes,all[row],attrib_id)));
                  attrib_id++;
              }
         }
         for(int row = 0;  row< k; row++)
         {
            System.out.print("centroid " + row + ": ");
            for(int col = 0; col < maxNum; col++)
            {
                 System.out.println(temp[row][col] + " ");
             }
            System.out.println();
        }
        return temp;
    }
    // end of randomCentroids function
 
 
// add function start
   static boolean adding(int[] list, int size, int value){
 
       for ( int idx=0; idx<size; idx++){
           if (list[idx] == value) //Does random value exist or not in list[]?
                   return false; //if same
       }
       list[size] = value;
       return true;
    }
   /*
    * end of add function
    */
 
 
// start cluster_array function
    static Vector[] cluster_Array(double [][] centroids, int k){
        //create given number of cluster
        Vector[] cluster_Array = new Vector[k];  // create k number of cluster array
        int closet_cluster = 0;       // to keep nearest centroid
        double closet_distance = 0.0;  // to keep nearest distance
        for(int idx=0; idx < k; idx++)
            cluster_Array[idx] = new Vector(); // insert each List into each cluster
 
        for (int idx = 0; idx < attributes.size(); idx++)
        {
             List fv1 = (List)attributes.get(idx);
             for (int idx1 = 0; idx1 < k; idx1++)
             {
                double[] fv2 = centroids[idx1];
                double distance = Double.valueOf(d2.format(distanceMetric(fv1, fv2))); // round to 2 decimal double value
                if (idx1==0)
                {
                    closet_distance = distance;
                    closet_cluster = idx1;
                }
                else if (idx1 > 0)
                {
                   if(distance <= closet_distance)
                    {
                        closet_distance = distance;
                        closet_cluster = idx1;
                    }
                }
 
             }
             cluster_Array[closet_cluster].add((List)attributes.get(idx));
        }
 
        return cluster_Array;
 }
 
 
    /*
     * Comparing clusters ... old cluster and new one
     * implemented by Thu Thu Tun
     */
 
     static boolean compare_centroids(double [][] c1, double[][] c2, int k, int maxNum){
     for (int idx = 0; idx < k; idx++)
         for (int idx1 = 0; idx1 < maxNum; idx1++)
            if (c1[idx][idx1] != c2[idx][idx1])
                return false;
 
       System.out.println("temp centroid");
       for (int idx = 0; idx < k; idx++)
        {
            System.out.println("temp centroid point " + idx);
            for (int idx1 = 0; idx1 < maxNum; idx1++)
            {
                System.out.print(idx1+". ");
                System.out.println(c1[idx][idx1]);
                System.out.print(" ");
 
            }
            System.out.println();
        }
 
     return true;
 }
 
 /**
   * Read SVMLight data file into Vector data structure
   *
   * @param fname
   * @return List = a list of feature vectors = [ [ label, [id, value], [id, value],...], .... ]
   *     a feature vector = [ label, [id, value], [id, value],...]
   *     label = int
   *     id = double
   *     value = double
   */
    private static List readSVMLightFile(String fname) {
		try{
			List featureV = new Vector();
			// Reading input by lines:
			BufferedReader in = new BufferedReader( new FileReader(fname) );
			String str = new String("");
			while ((str = in.readLine()) != null){
			str = str.trim();
			if(str.length() > 0){
				String line[];
				String fs[];
				line = str.split("\\s",2);
				String clabel = line[0].trim().replace("+", "");
				fs = line[1].trim().split("\\s");   // feature list #:v ...
				List fl = new Vector();
				fl.add(Integer.parseInt(clabel));
			for(int idx = 0; idx < fs.length; idx++)
			{
				double f[] = {0,0};
				String attr[] = fs[idx].split(":");
				int aid = Integer.valueOf(attr[0].trim());
				if( aid > maxNum) maxNum = aid;
				f[0] = aid;                             // attribute id
				f[1] = Double.valueOf(attr[1].trim());    // attribute value
				fl.add(f);
			}
			featureV.add(fl);
        }
      }
      in.close();
      return featureV;
    }
    catch(IOException e){
        System.out.println("Error reading file: " + e);
        return null;
    }
    }//end of read file
 
   /**
   * Calculate Euclidean distance metric between two sparse feature vectors
   * @param i   feature vector = [ label, [id, value], [id, value],...]
   * @param j   feature vector = [ label, [id, value], [id, value],...]
   * modified by Thu Thu Tun
   * @return
   */
  static double distanceMetric(List fv1, double[] fv2){
		double[] fv = new double[maxNum];
		for(int idx = 0; idx < maxNum; idx++){
			fv[idx] = 0;
		}
		Iterator itr = fv1.iterator();
		int k = 0;
		while(itr.hasNext())
		{
		if( k > 0){
			double f[] = (double[])itr.next();
			fv[(int)f[0]-1] = f[1];
		}
		else
			itr.next();
		k++;
    }
 
 
    for (int idx = 0; idx < maxNum; idx++)
    {
      fv[idx] -= fv2[idx];
    }
    double distance = 0;
    for(int idx = 0; idx < maxNum; idx++)
    {
      distance += Double.valueOf(d2.format(fv[idx]*fv[idx]));
    }
 
    return Double.valueOf(d2.format(Math.sqrt(distance)));
    }
 
 
   /**
   * Retrive the attribute value
   * @param attributes
   * @param data_index   0 based: 0,...,dataSize-1
   * @param attribute_id 1 based: 1,...,dimension
   * @return
   */
  static double getAttributeValue(List attributes, int data_index, int attribute_id)
  {
    List fv = (List)attributes.get(data_index);  // feature vector
 
    Iterator itr = fv.iterator();
    int k = 0;
    while(itr.hasNext())
    {
      if( k > 0)
      {
        double f[] = (double[])itr.next();
        if( attribute_id == (int)f[0] )
        {
          return f[1];
        }
      }
      else
        itr.next();
      k++;
    }
 
    return 0;
  }
 
   /**
   * Retrive the class label
   * @param attributes
   * @param data_index   0 based: 0,...,dataSize-1
   * @return
   */
  static int getClassLabel(List attributes, int data_index)
  {
    List fv = (List)attributes.get(data_index);  // feature vector
    return (Integer)fv.get(0);
  }
}