Abstract

The application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. This paper present the basic data mining techniques i. e. , naive Bayesian tree, RIpple DOwn Rule, naive Bayes and decision tree algorithm J48 for classifying in medical databases. The goal of this paper is to provide a comprehensive of different classifying techniques in data mining. To evaluate the performance of the above techniques recall, precision and accuracy measures are applied.

References

• Mikut, R. , Reischl, M. 2011. Data mining and knowledge discovery, Wiley Interdisciplinary Reviews, pp. 431-443.
• Barrett, T. , Troup, D. , Wilhite, S. , Ledoux, P. , Rudnev, D. , Evangelista, C. , Kim, I. , Soboleva, A. , Tomashevsky, M. , Edgar, R. 2007. NCBI GEO: Mining tens of millions of expression profiles-database and tools update, Nucleic Acids Re. , pp. 760-765.
• Cover, T. , Hart, P. 1967. Nearest neighbor pattern classification, IEEE Transactions on Information Theory, pp. 21–27.
• Darwiche, A. 2009. Modeling and reasoning with Bayesian networks, Cambridge University Press, pp. 1-562.
• Castro, J. R. , Castillo, O. , Martinez, L. G. 2007. Interval Type-2 Fuzzy Logic Toolbox, Engineering Letters, pp. 89-98.
• Minaei-Bidgoli, B. , Punch, W. 2003. Using genetic algorithms for data mining optimization in an educational web-based system, Genetic and Evolutionary Computation, pp: 2252-2263.
• Sunita, B. , Jitender, A. 2012. Classification and feature selection techniques in data mining, International Journal of Engineering Research & Technology, pp. 1-6.
• Yongheng, Z. , Yanxia, Z. 2007. Comparison of decision tree methods for finding active object, Advances of Space Research, pp. 1955-1959.
• Jiang, S. , Harry, Z. 2006. A fast decision tree learning algorithm, In Proc. of the National Conference on Artificial Intelligence, American Association for Artificial Intelligence, pp. 1-6.
• Datta, R. P. , Sanjib, S. 2011. An empirical comparison of rule based classification techniques in medical databases, Working Paper, Indian Institute of Foreign Trade, pp. 1-18.
• Choochart, H. 2008. A tutorial on naïve Bayes classification, pp. 1-6.
• Yogendra, K. J. , Upendra. 2012. An efficient intrusion detection based on decision tree classifier using feature reduction, International Journal of Scientific and Research Publications, pp. 1-6.
• Samuel, A. M. , Daniel, M. D. , James, K. , Gray, M. W. 2009. Are decision trees always greener on the open (source) side of the fence?, In Proc. of the Inter. Conf. on Data Mining. pp. 185–188.
• Hall, J. , Frank, E. , Holmes, G. , Pfahringer, B. , Reutemann, P. , Witten, I. 2009. The WEKA data mining software: an update, ACM SIGKDD Explorations Newsletter, pp. 10-18.
• Tina, R. P. , Sherekar, S. S. 2013. Performance analysis of naïve Bayes and J48 classification algorithm for data classification, Inter. Jour. of Computer Science and Applications, pp. 256-261.
• Aman, K. S. , Suruchi, S. 2011. A comparative study of classification algorithms for spam email data analysis, Inter. Jour. of Comp. Sci. and Eng. , pp. 1890-1895.