Algorithm 1. DBSCAN Clustering Process

1. Begin 2. Input: 3.  U = {p1,p2,…pn}, MinPoints, 4.      Output: 5. C1,C2,…Ck//clusters descended by number of elements 6.  M = {m1,m2,…mn}//set of noises 7.  k = 0, l = 0 8.  for ( i = 1; i<= n; i ++) 9.    if $\sum 1_{\{q\in \mathsf{\Omega }|dist(q,p_i)<\epsilon \}}\ge \mathit{MinPoints}$ 10.  k = k + 1 11.  else 12.  if $\sum 1_{\{q\in \mathsf{\Omega }|dist(q,p_i)<\epsilon \}}\ge \mathit{MinPoints}$ 13.      l = l+1 14.      $m_k=p_i$ 15. end