Back
 IJIS  Vol.3 No.1 A , March 2013
Personalized Action Rules for Side Effects Object Grouping
Abstract: There have been multiple techniques to discover action-rules, but the problem of triggering those rules was left exclusively to domain knowledge and domain experts. When meta-actions are applied on objects to trigger a specific rule, they might as well trigger transitions outside of the target action rule scope. Those additional transitions are called side effects, which could be positive or negative. Negative side effects could be devastating in some domains such as healthcare. In this paper, we strive to reduce those negative side effects by extracting personalized action rules. We proposed three object-grouping schemes with regards to same negative side effects to extract personalized action rules for each object group. We also studied the tinnitus handicap inventory data to apply and compare the three grouping schemes.
Cite this paper: H. Touati, J. Kuang, A. Hajja and Z. Ras, "Personalized Action Rules for Side Effects Object Grouping," International Journal of Intelligence Science, Vol. 3 No. 1, 2013, pp. 24-33. doi: 10.4236/ijis.2013.31A004.
References

[1]   Z. W. Ras and A. Wieczorkowska, “Action-Rules: How to Increase Profit of a Company,” In: D. A. Zighed, J. Komorowski and J. Zytkow, Eds., Principles of Data Mining and Knowledge Discovery, Springer, Lyon, Vol. 1910, 2000, pp. 587-592.

[2]   A. Hajja, A. Wieczorkowska, Z. W. Ras and R. Gubrynowicz, “Object-Driven Action Rules and Their Application to Hypernasality Treatment,” Proceedings of ECMLPKDD Workshop on New Frontiers in Mining Complex Patterns, Bristol, 24-28 September 2012, pp. 104-115.

[3]   Z. W. Ras, A. Dardzinska, L. S. Tsay and H. Wasyluk, “Association Action Rules,” IEEE International Conference on Data Mining Workshops, Pisa, 15-19 December 2008, pp. 283-290.

[4]   H. Wasyluk, Z. W. Ras and E. Wyrzykowska, “Application of Action Rules to HEPAR Clinical Decision Support System,” Journal of Experimental and Clinical Hepatology, Vol. 4, No. 2, 2008, pp. 46-48.

[5]   X. Zhang, Z. W. Ras, P. J. Jastreboff and P. L. Thompson, “From Tinnitus Data to Action Rules and Tinnitus Treatment,” Proceedings of 2010 IEEE Conference on Granular Computing, IEEE Computer Society, Silicon Valley, 2010, pp. 620-625.

[6]   S. Greco, B. Matarazzo, N. Pappalardo and R. Slowinski, “Measuring Expected Effects of Interventions Based on Decision Rules,” Journal of Experimental and Theoretical Artificial Intelligence, Vol. 17, No. 1-2, 2005, pp. 103-118. doi:10.1080/09528130512331315864

[7]   Z. He, X. Xu, S. Deng and R. Ma, “Mining Action Rules from Scratch,” Expert Systems with Applications, Vol. 29, No. 3, 2005, pp. 691-699. doi:10.1016/j.eswa.2005.04.031

[8]   Y. Qiao, K. Zhong, H.-A. Wang and X. Li., “Developing Event-Condition-Action Rules in Real-Time Active Database,” Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, 11-15 March 2007, pp. 511- 516.

[9]   Z. Ras and A. Dardzinska, “From Data to Classification Rules and Actions,” International Journal of Intelligent Systems, Vol. 26, No. 6, 2011, pp. 572-590.

[10]   J. Rauch and M. Simunek, “Action Rules and the GUHA Method: Preliminary Considerations and Results,” Foundations of Intelligent Systems, Vol. 5722, 2009, pp. 76-87.

[11]   K. Wang, Y. Jiang and A. Tuzhilin, “Mining actionable Patterns by Role Models,” Proceedings of 22nd International Conference on Data Engineering, Atlanta, 3-7 April 2006, pp. 16-26.

[12]   H. Zhang, Y. Zhao, L. Cao and C. Zhang, “Combined Association Rule Mining,” In: T. Washio, E. Suzuki, K. M. Ting and A. Inokuchi, Eds., Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS, Springer, Vol. 5012, 2008, pp. 1069-1074.

[13]   J. Rauch, “Action4ft-Miner Module,” Lisp-Miner Project, 2012. http://lispminer.vse.cz

[14]   Z. Pawlak, “Information Systems—Theoretical Foundations,” Information Systems Journal, Vol. 6, No. 3, 1981, pp. 205-218. doi:10.1016/0306-4379(81)90023-5

[15]   Z. W. Ras, E. Wyrzykowska and H. Wasyluk, “ARAS: Action Rules Discovery Based on Agglomerative Strategy,” Mining Complex Data, Vol. 4944, 2008, pp. 196-208.

[16]   Z. W. Ras and A. Dardzinska, “Action Rules Discovery Based on Tree Classifiers and Meta-Actions,” Foundations of Intelligent Systems, Vol. 5722, 2009, pp. 66-75. doi:10.1007/978-3-642-04125-9_10

[17]   A. Tzacheva and Z. W. Ras, “Association Action Rules and Action Paths Triggered by Meta-Actions,” IEEE Conference on Granular Computing, San Jose, 14-16 August 2010, pp. 772-776.

 
 
Top