Many companies like credit card, insurance,
bank, retail industry require direct marketing. Data mining can help those institutes
to set marketing goal. Data mining techniques have good prospects in their target
audiences and improve the likelihood of response. In this work we have investigated
two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms.
The goal of this work is to predict whether a client will subscribe a term deposit.
We also made comparative study of performance of those two algorithms. Publicly
available UCI data is used to train and test the performance of the algorithms.
Besides, we extract actionable knowledge from decision tree that focuses to take
interesting and important decision in business area.
Cite this paper
M. Karim and R. Rahman, "Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing," Journal of Software Engineering and Applications
, Vol. 6 No. 4, 2013, pp. 196-206. doi: 10.4236/jsea.2013.64025
 C. X. Ling and C. Li, “Data Mining for Direct Marketing: Problems and Solutions,” Proceedings of International Conference on Knowledge Discovery from Data (KDD 98), New York City, 27-31 August 1998, pp. 73-79.
 G. Dimitoglou, J. A. Adams and C. M. Jim, “Comparison of the C4.5 and a Naive Bayes Classifier for the Prediction of Lung Cancer Survivability,” Journal of Computing, Vol. 4, No. 2, 2012, pp. 1-9.
 P. S. Vadivu and V. K. David, “Enhancing and Deriving Actionable Knowledge from Decision Trees,” International Journal of Computer Science and Information Security (IJCSIS), Vol. 8, No. 9, 2010, pp. 230-236.
 M. Alam and S. A. Alam, “Actionable Knowledge Mining from Improved Post Processing Decision Trees,” International Conference on Computing and Control Engineering (ICCCE 2012), Chennai, 2012, pp. 1-8.
 R. A. Patil, P. G. Ahire, P. D. Patil, Avinash and L. Golande, “Decision Tree Post Processing for Extraction of Actionable Knowledge,” International Journal of Engineering and Innovative Technology (IJEIT), Vol. 2, No. 1, 2012, pp. 152-155.
 Q. Yang, J. Yin, C. Ling and R. Pan, “Extracting Actionable Knowledge from Decision Trees,” IEEE Transaction on Knowledge and Data Engineering, Vol. 19, No. 1, 2007, pp. 43-56.
 Q. Yang, J. Yin, C. X. Ling and T. Chen, “Post Processing Decision Trees to Extract Actionable Knowledge,” Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03), 19-22 November 2003, Florida, pp. 685-688. doi:10.1109/ICDM.2003.1251008
 Z. He, X. Xu and S. Deng, “Data Mining for Actionable Knowledge: A Survey,” ArXiv Computer Science e-Prints, 2005.
 J. Huang, J. Lu and C. X. Ling, “Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy,” Proceedings of Third IEEE International Conference on Data Mining (ICDM 2003), 19-22 November 2003, pp. 553-556. doi:10.1109/ICDM.2003.1250975
 A. Abrahams, F. Hathout, A. Staubli and B. Padmanabhan, “Profit-Optimal Model and Target Size Selection with Variable Marginal Costs,” 2013.
 Weka Data Mining Tool.
 UCI Machine Repository Data.
 H. Kaur, “Actionable Rules: Issues and New Directions,” World Academy of Science, Engineering and Technology, Vol. 5, 2005, pp. 61-64.
 T. Goan and S. Henke, “From Data to Actionable Knowledge: Applying Data Mining to the Problem of Intrusion Detection,” Proceedings of International Conference on Artificial Intelligence (IC-AI’2000), Las Vegas.
 R. D’Souza, M. Krasnodebski and A. Abrahams, “Implementation Study: Using Decision Tree Induction to Discover Profitable Locations to Sell Pet Insurance for a Startup Company,” Journal of Database Marketing & Customer Strategy Management, Vol. 14, No. 4, 2007, pp. 281-288. doi:10.1057/palgr ave.dbm.3250059