function centroids = clustering_pc(points, NC)
    % TODO K-Means code
      [m n] = size(points);
      clusters = zeros(NC,n);
      %step 1
      for i = 1 : NC
        for j = 1 : n
        idx = randi(m);
        temp(i,:) = centroids(i,:) = points(randi(m),:);
        endfor
      endfor
      dist = zeros(m,1);
      n = 0
      %step 2
    for iter = 1 : 1000
        for i = 1: m
         for j = 1: n
           for k = 1: NC
           dist(k,:) = norm(centroids(k,:) - points(i,:));
            endfor
            [index] = min(min(dist));
            for k = 1: NC
              if (k == index)
                clusters(k,:) = points(i,:) + clusters(i,:);

              endif
              centroids(k,:) = mean(clusters);
              endfor
        endfor
      endfor
      if temp != centroids
        temp = centroids;
      else
        break;
      endif
    endfor

    end