A. Sleit, S. Al-Adaileh, N. Al-Omari, and H. Hurani


  1. [1] G. Sheikholeslami, S. Chatterjee, & A. Zhang, Wavecluster: A multi-resolution clustering approach for very large spatial databases, Proc. of VLDB, NYC, NY, 1998.
  2. [2] M. Ester, H. Kriegel, J. Sander, & X. Xu, A density based algorithm for discovering clusters in large spatial databases with noise, Proc. of VLDB, Bombay, India, 1996.
  3. [3] A. Hinneburg & D. Keim, An efficient approach to clustering in large multimedia databases with noise, Proc. of KDD, NYC, NY, 1998.
  4. [4] G. Guha, R. Rastogi, & K. Shim, CURE: An efficient clustering algorithm for large databases, Proc. of the 1998 ACM SIGMOD, Seattle, WA, 1998.
  5. [5] T. Zhang, R. Ramakrishnan, & M. Livny, BIRCH: An efficient data clustering method for very large databases, Proc. of SIGMOD, Montreal, Canada, 1996, 103–114.
  6. [6] F.W. Yang, H.J. Lin, & S.H. Yen, An improved unsupervised clustering algorithm based on population Markov chain, International Journal of Computers and Applications, 202, 2007, 202–210.
  7. [7] R. Mitchell, Mean-tracking as an efficient preprocessor for the K-means clustering algorithm, Artificial Intelligence and Soft Computing, 357, 2002, 78–83.
  8. [8] K. Chen & L. Liu, Cluster rendering of skewed datasets via visualization, Proc. of ACM Symposium on Applied Computing, Melbourne, 2003.

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