Home > Archive > 2011 > Volume 1 Number 5 (Dec. 2011) >
IJMLC 2011 Vol.1(5): 528-533 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.79

A Bayesian Network Approach for Causal Action Rule Mining

Pirooz Shamsinejad and Mohamad Saraee

Abstract—Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. Action Rule is a new tool in this research area that suggests some actions to user to gain a profit in his/her domain. Up to now some methods have been devised for action rule mining. Decision Trees, Classification Rules and Association Rules are three learner machines that already have been used for action rule mining. But when we want to suggest an action we need to know the causal relationships among parameters and current methods can’t say anything about that. So that we use here Bayesian Networks as one of the most powerful knowledge representing models that can show the causal relationships between variables of interest for extracting action rules. Another benefit of new method is about the background knowledge. Bayesian Networks are very powerful at integrating the background knowledge into model. At the end of this paper an action rule mining system is proposed that can suggest the most profitable action rules for each case or class of cases.

Index Terms—Actionable Knowledge Discovery, Action Rule Mining, Bayesian Networks, Causal Action Rule.

Pirooz Shamsinejad is a Ph.D. candidate at Electrical and Computer Engineering Department of Isfahan University of Technology, Isfahan, Iran (e-mail: p_shamsinejad@ec.iut.ac.ir). Mohammad Saraee is the founder and Director of the Intelligent Databases, Data Mining and Bioinformatics Research Centre.


Cite:Pirooz Shamsinejad and Mohamad Saraee, "A Bayesian Network Approach for Causal Action Rule Mining," International Journal of Machine Learning and Computing vol.1, no. 5, pp. 528-533, 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

Article Metrics