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A Comparative Study on MMDBM Classifier Incorporating Various Sorting Procedure


  • Department of Mathematics, College of Engineering Guindy, Anna University, Chennai - 600 025, Tamil Nadu, India
  • Department of Mathematics, Indian Institute of Technology Chennai, Chennai - 600 036, Tamil Nadu, India


Classification is one of the most important methods in data mining. These methods are used to extract meaningful information from large database which can be effectively used for predicting unknown class. The classifier based on decision tree is called Mixed Mode Data Base Miner (MMDBM) which is tested with different sorting techniques (merge sort, quick sort, radix sort) to compare the processing time to SLIQ classifier. In this paper, we carried out a comparative study on MMDBM classifier incorporating various sorting procedure by using Blood Pressure (BP) database, and finally the proposed method MMDBM classifier is one of the best classifiers among SLIQ supervised learning method. This proposed method achieved less processing time and higher rate of accuracy.


Classification, Data Mining, Java, Sorting.

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  • Agarwal R, Srikant R. Fast Algorithm for mining association rules. Proceedings of International Conference on Very Large Data Bases. 1994 Sep. p. 487–99.
  • Ai-Hegami A. Pruning based interestingness of mined classification patterns. Int Arab J Inform Tech. 2009; 6(4):336–43.
  • Breiman L, et al. Classification and regression Trees. Belmont: Wadsworth; 1984
  • Chandrashekar A, Vijay Kumar J. Data mining based hybrid intrusion detection system. Indian Journal of Science and Technology. 2014; 7(6):781–9.
  • Carvalhoa DR, Freitasb AA. A hybrid decision tree/genetic algorithm method for data mining. Inform Sci. 2004; 163(1–3):13–35.
  • Fayyad UM, Piatesky-Shapiro G, Smith. Advances in knowledge discovery in database. Cambridge, Massachusetts: AAAI/MIT Press; 1996.
  • Han J, Cai Y, Cercone N. Data-driven discovery of quantitative rules in relational databases. IEEE Trans Knowl Data Eng. 1993; 5(1):26–40.
  • Joachims T. Text categorization with support vector machines: learning with many relevant feature. Machine Learning: ECML'98. Berlin, Heidelberg: Springer; 1998. p. 137–42.
  • Mehta M, Agarwal R, Rissanen J. SLIQ: A fast scalable classifier for data mining. International Conference on Extending Database Technology (EDBT’96), Avignon, France; 1996. p. 18–32.
  • Omer. A rule induction algorithm for knowledge discovery and classification. Turk J Elec Eng Comput Sci. 2013; 21:1223–41.
  • Piatetsky-Shapiro G, Frawley WJ. Knowledge discovery in databases. Cambridge, Massachusetts: AAAI/MIT Press; 1991.
  • Quinlan J. C4.5: Programs for Machine Learning. San Francisco, CA, USA: The Morgan Kaufmann; 1993. p. 235–40.
  • Abraham RM, Beeda AR, Manjula R. Data mining: building social network Sayali Nishikant Chakradeo. Indian Journal of Science and Technology. 2015; 8(S2):212–6.
  • Iqbal R, Azmi Murad MA, Mustapha A, Panahy PHS, Khanahmadliravi N. An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology. 2013; 6(3):4219–25.
  • Shafer J, Agrawal R, Metha M. SPRINT: a scalable parallel classifier for data mining. Proceedings of the 22nd VLDB Conference Mumbai, India; 1996.
  • Sundar S, Srikanth D, Shanmugam MS. A new predictive classifier for improved performance in data mining: object oriented design and implementation. Proceedings of the International Conference on Industrial Mathematics, IIT Bombay. New Delhi: Narosa; 2006. p. 491–514.
  • Weiss SM, Kulikowski CA. Computer system that learning: classification and prediction method from statistics, neural Nets. Machine Learning, and Expert System. San Francisco, CA, USA: Morgan Kaufman; 1991.


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