Abstract
Action rules extraction is a field of data mining used to extract actionable patterns from large datasets. Action rules present users with a set of actionable tasks to follow to achieve a desired result. An action rule can be seen as two patterns of feature values (classification rules) occurring together and having the same features. Action rules are evaluated using their supporting patterns occurrence in a measure called support. They are also evaluated using their confidence defined as the product of the two patterns confidences. Those two measures are important to evaluate action rules; nonetheless, they fail to measure the feature values transition correlation and applicability. This is due to the core of the action rules extraction process that extracts independent patterns and constructs an action rule. In this chapter, we present the benefits of meta-actions in evaluating action rules in terms of two measures, namely likelihood and execution confidence. In fact, in meta-actions, we extract real feature values transition patterns, rather than composing two feature values patterns. We also present an evaluation model of the application of meta-actions based on cost and satisfaction. We extracted action rules and meta-actions and evaluated them on the Florida State Inpatient Databases that is a part of the Healthcare Cost and Utilization Project.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases. VLDB’94, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Cost, H., (HCUP), U.P.: HCUP state inpatient databases (SID), agency for healthcare research and quality, rockville, md. www.hcup-us.ahrq.gov/sidoverview.jsp (2005–2009)
Cost, H., (HCUP), U.P., for Healthcare Research, A., Quality: Clinical classifications software (CCS) for ICD-9-CM. Website. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp
Im, S., Raś, Z.: Action rule extraction from a decision table: ARED. In: Foundations of Intelligent Systems. Proceedings of ISMIS’08, pp. 160–168. Springer, Toronto (2008)
Kohli, D., Raś, Z., Thompson, P., Jastreboff, P., Wieczorkowska, A.: From music to emotions and tinnitus treatment, initial study. In: Foundations of Intelligent Systems. Proceedings of ISMIS 2012 Symposium, pp. 244–253. Springer (2012)
Qiao, Y., Zhong, K., Wang, H., Li, X.: Developing event-condition-action rules in real-time active database. In: Proceedings of the 2007 ACM Symposium on Applied Computing. SAC ’07, pp. 511–516. ACM, New York (2007)
Raś, Z., Dardzińska, A.: Action rules discovery based on tree classifiers and meta-actions. In: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems. ISMIS’09, pp. 66–75. Springer, Berlin (2009)
Raś, Z., Dardzinska, A.: From data to classification rules and actions. Int. J. Intell. Syst. 26(6), 572–590 (2011)
Raś, Z., Dardzinska, A., Tsay, L., Wasyluk, H.: Association action rules. In: Proceedings of IEEE International Conference on Data Mining Workshops. ICDMW ’08, pp. 283–290 (2008)
Raś, Z., Wieczorkowska, A.: Action-rules: how to increase profit of a company. In: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery. PKDD’00, pp. 587–592. Springer, London (2000)
Raś, Z., Wyrzykowska, E., Wasyluk, H.: ARAS: action rules discovery based on agglomerative strategy. In: Proceedings of the 3rd ECML/PKDD International Conference on Mining Complex Data. MCD’07, pp. 196–208. Springer, Berlin (2008)
Rauch, J., Šimůnek, M.: Action rules and the Guha method: preliminary considerations and results. In: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems. ISMIS’09, pp. 76–87. Springer, Berlin (2009)
Tzacheva, A., Raś, Z.: Association action rules and action paths triggered by meta-actions. In: Proceedings of the 2010 IEEE International Conference on Granular Computing. GRC’10, pp. 772–776. IEEE Computer Society, Washington (2010)
Wang, K., Jiang, Y., Tuzhilin, A.: Mining actionable patterns by role models. In: Proceedings of the 22nd International Conference on Data Engineering. ICDE ’06, pp. 16–16 (2006)
Wasyluk, H., Raś, Z., Wyrzykowska, E.: Application of action rules to Hepar clinical decision support system. J. Exp. Clin. Hepatol. 4(2), 46–48 (2008)
Zhang, H., Zhao, Y., Cao, L., Zhang, C.: Combined association rule mining. In: Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. PAKDD’08, pp. 1069–1074. Springer, Berlin (2008)
Zhang, X., Raś, Z., Jastreboff, P., Thompson, P.: From Tinnitus data to action rules and Tinnitus treatment. In: Proceedings of IEEE International Conference on Granular Computing (GrC), pp. 620–625 (2010)
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Touati, H., Raś, Z.W., Studnicki, J. (2015). Meta-actions as a Tool for Action Rules Evaluation. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_9
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DOI: https://doi.org/10.1007/978-3-662-45620-0_9
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