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Mining Meta-Actions for Action Rules Reduction

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Meta-actions effect and selection are the fundamental core for a successful action rule execution. All atomic action terms on the left-hand side of an action rule have to be covered by well chosen meta-actions in order for it to be executed. The choice of meta-actions depends on the antecedent side of action rules; however, it also depends on their list of atomic actions that are outside of the action rule scope, seen as side effects. In this paper, we strive to minimize the side effects by decomposing the left-hand side of an action rule into executable action rules covered by a minimal number of meta-actions and resulting in a cascading effect. This process was tested and compared to original action rules. Experimental results show that side effects are diminished in comparison with the original meta-actions applied while keeping a good execution confidence. ding effect. This process was tested and compared to original action rules. Experimental results show that side effects are diminished in comparison with the original meta-actions applied while keeping a good execution confidence.
Wydawca
Rocznik
Strony
225--240
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
Twórcy
autor
  • College of Computing and Informatics, University of North Carolina, Charlotte, NC 28223, USA
autor
  • College of Computing and Informatics, University of North Carolina, Charlotte, NC 28223, USA
  • Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland
Bibliografia
  • [1] Agrawal, R., Srikant, R., Fast algorithm for mining association rules, Proceedings of the Twentieth Internationsl Conference on VLDB, 1994, 487-499
  • [2] Greco, S., Matarazzo, B., Pappalardo, N., Slowinski, R., Measuring expected effects of interventions based on decision rules, Journal of Experimental and Theoretical AI, Taylor and Francis, Vol. 17, No. 1-2, 2005, 103-118.
  • [3] He, Z., Xu, X., Deng, S., Ma R., Mining action rules from scratch, Expert Systems with Applications, Elsevier, Vol. 29, No. 3, 2005, 691-699.
  • [4] Ling C. X., Chen T., Yang Q., ChenJ., Mining optimal action for intelligent CRM, Proceedings of ICDM02, 767-770, 2002.
  • [5] Pawlak, Z., Information systems - theoretical foundations Information Systems Journal, Vol. 6, 1981, 205218.
  • [6] Qiao, Y., Zhong, K., Wang, H.-A., Li., X., Developing event-condition-action rules in real-time active database, Proceedings of the 2007 ACM Symposium on Applied Computing, ACM, 2007, 511-516.
  • [7] Rauch, J., Action4ft-Miner Module, Lisp-Miner Project, http://lispminer.vse.cz/
  • [8] Rauch, J., Simunek, M., Action rules and the GUHA method: preliminary considerations and results, Foundations of Intelligent Systems, Proceedings of ISMIS, LNAI, Vol. 5722, Springer, 2009, 76-87
  • [9] Ras, Z.W., Dardzinska, A., Action Rules Discovery based on Tree Classifiers and Meta-Actions, in Foundations of Intelligent Systems, Proceedings of ISMIS’09, (Eds. J. Rauch et al), LNAI, Vol. 5722, Springer, 2009, 66-7
  • [10] Ras, Z., Dardzinska, A., From data to classification rules and actions, International Journal of Intelligent Systems 26(6), Wiley, 2011, 572-590.
  • [11] Ras, Z.W., Dardzinska, A., Tsay, L.-S., Wasyluk, H., Association action rules. IEEE/ICDM Workshop on Mining Complex Data, (MCD 2008), Pisa, Italy, ICDM Workshops Proceedings, IEEE Computer Society, 2008, 283-290.
  • [12] Ras, Z.W., Wieczorkowska, A., Action-Rules: How to increase profit of a company, Zighed, D.A., Komorowski, J., Zytkow, J. (eds.), PKDD 2000, LNAI, Vol. 1910,2000, 587-592.
  • [13] Ras, Z.W., Wyrzykowska, E., Wasyluk, H., ARAS: Action rules discovery based on Agglomerative Strategy, Mining Complex Data, Post-Proceedings of 2007 ECML/PKDD Workshop, Springer, LNAI, vol. 4944,2008, 196-208.
  • [14] Tzacheva, A., Ras, Z.W., Association Action Rules and Action Paths Triggered by Meta-Actions, in Proceedings of 2010 IEEE Conference on Granular Computing, Silicon Valley, CA, IEEE Computer Society, 2010, 772-776
  • [15] Wang, K., Jiang, Y., Tuzhilin, A., Mining actionable patterns by role models, Proc. 22nd International Conference on Data Engineering, 3-7 April, 2006,16-26.
  • [16] Wasyluk, H., Ras, Z.W., Wyrzykowska, E., Application of action rules to HEPAR Clinical Decision Support System, Journal of Experimental and Clinical Hepatology, 4(2), 2008, 46-48
  • [17] Wright, O., Wright, W., Flying-Machine, US Patent No. 821393, 1906.
  • [18] Zhang, H., Zhao, Y., Cao, L., Zhang, C., Combined association rule mining, Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.), PAKDD 2008, LNCS, Vol. 5012, Springer, 2008, 1069-1074.
  • [19] Zhang, X., Ras, Z.W., Jastreboff, P.J., Thompson, P.L., From tinnitus data to action rules and tinnitus treatment. Proceedings of 2010 IEEE Conference on Granular Computing, Silicon Valley, CA, IEEE Computer Society, 2010, 620-625.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-32f7b5cc-9cd5-47bc-950c-e675d51304b9
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