Abstract

In data mining, decision trees are considered to be most popular approach for classifying different attributes. The issue of growing of decision tree from available data is considered in various discipline like pattern recognition, machine learning and data mining. This paper presents an updated survey of growing of decision tree in two phases following a top down approach. This paper presents a framework for dealing with the condition of uncertainty during decision tree induction process and concludes with comparison of Averaging based approach and Uncertain Decision Tree approach.

References

• Fayyad, U. , Piatetsky-Shapiro, G. , & Smyth, P. (2006, March). "From Data Mining to Knowledge Discovery in Databases". The Knowledge Engineering Review,Vol 21, No. 1, pp. 1-24.
• Han, J. , & Kamber, M. (2006). "Data Mining: Concepts and Techniques" (2nd ed. ). Morgan Kaufmann Publishers.
• Pujari, A. K. (2001). "Data Mining Techniques". Universites Press India Private Limited.
• Agarwal, R. , Imieli?ski, T. , & Swami, A. (1993, June). "Mining association rules between sets of items in large databases". ACM SIGMOD Record, Vol 22,No. 2, pp. 207-216.
• Zhao, Q. , & Bhowmick, S. S. (2003). "Association Rule Mining: A Survey". Retrieved fromhttp://sci2s. ugr. es/keel/pdf/specific/report/zhao03ars. pdf
• Oracle® Data Mining Concepts 11g. (2008, May). Retrieved from oracle. com: http://docs. oracle. com/cd/B28359_01/datamine. 111/b28129. pdf
• Lavanya, D. , & Rani, U. k. (2011, July). "Performance Evaluation of Decision Tree Classifiers on Medical Datasets". International Journal of Computer Applications, Vol 26,No. 4,pp. 1-4.
• Kotsiantis, S. B. (2007). "Supervised Machine Learning : A review of classification techniques",Vol 160,No. 3,Frontiers in Artificial Intelligence and Applications
• Barros, R. C. , Basgalupp, M. P. , Carvalho, A. C. , &Freitas, A. A. (2010, Jan). "A Survey of Evolutionary Algorithms for DecisionTree Induction". IEEE Transactions on Systems,Mans and Cybernetics, Vol. 10,No. 10,pp. 1-22.
• Quinlan, J. R. (1987). "Generating production rules from decision trees". Proceedings of the 10th international joint conference on Artificial intelligence , pp. 304-307.
• Maimon, O. , & Rokach, L. (2010). "Data Mining and Knowledge Discovery Handbook". (2nd, Ed. ) Springer.
• De ville, B. (2006). "Decision trees for Business Intelligent and Data Mining using SAS Enterprise Miner". NC, USA: SAS Institute Inc.
• Apers, P. , Bouzeghoub, M. , & Gardarin, G. (1996). Advances in Database Technology. 5th International Conference on Extending Database Technology Avignon,Vol. 1057.
• Tsang, S. , Kao, B. , Yip, K. Y. , Ho, W. -S. , & Lee, S. D. (2011, Jan). "Decision Trees for Uncertain Data". IEEE Transactions on Knowledge and Data Engineering, Vol 23,No. 1.
• Bramer, M. (2007). Principles of data Mining. London: Springer.
• Quinlan, J. R. (1993). "C4. 5 programs for machine learning". Morgan, .
• Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, Vol 1,No. 1,pp. 81-106
• Harris, E. (2002). "Information Gain Versus Gain Ratio: A Study of Split Method Biases". AMAI.
• Das, S. , & Saha, B. (2009). "Data Quality Mining using Genetic Algorithm". International Journal of Computer Science and Security, Vol 3,No. 2,pp. 105-112.
• Anyanwu , M. N. , & Shiva, S. G. (2009). "Comparative Analysis of Serial Decision Tree Classification". International Journal of Computer Science and Security, Vol 3,No. 3,pp. 230-239.
• Venkatadri, & Lokanatha. (2011). "A Comparative Study of Decision Tree Classification Algorithms in Data Mining. ". International Journal of Computer Applications in Engineering, Technology and Sciences, Vol 3,No. 3. ,pp. 230-240
• Ruggieri, S. (2002, April). "Efficient C4. 5 [classification algorithm]". IEEE Tansactions on Knowledge and Data Engineering,pp. 438-444.
• C5 Algorithm. (2012). Retrieved fromhttp://rulequest. com/see5-comparison. html