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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.

has been cited by the following article:

• TITLE: Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

  AUTHORS: Masud Karim, Rashedur M. Rahman

  KEYWORDS: CRM; Actionable Knowledge; Data Mining; C4.5; Naïve Bayes; ROC; Classification

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.4, April 29, 2013

  ABSTRACT: 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.