Abstract
Action rules introduced in [12] and extended further to e-action rules [21 have been investigated in [22], [13], [20]. They assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and our goal is to re-classify objects from one of these classes into the other one. Flexible attributes are essential in achieving that goal since they provide a tool for making hints to a user what changes within some values of flexible attributes are needed for a given set of objects to re-classify them into a new decision class. There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures [8], [1], [14], [15], [23]. In this paper we focus on a cost of an action rule which was introduced in [22] as an objective measure. An action rule was called interesting if its cost is below and support higher than some user-defined threshold values. We assume that our attributes are hierarchical and we focus on solving the failing problem of interesting action rules discovery. Our process is cooperative and it has some similarities with cooperative answering of queries presented in [3], [5], [6].
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Discovery of actionable patterns in databases: the action hierarchy approach. In: Proceedings of KDD 1997 Conference, Newport Beach, CA. AAAI Press, Menlo Park (1997)
Chmielewski, M.R., Grzymala-Busse, J.W., Peterson, N.W., Than, S.: The Rule Induction System LERS - a version for personal computers in Foundations of Computing and Decision Sciences, vol. 18(3-4), pp. 181–212. Institute of Computing Science, Technical University of Poznan, Poland (1993)
Chu, W., Yang, H., Chiang, K., Minock, M., Chow, G., Larson, C.: Cobase: A scalable and extensible cooperative information system. Journal of Intelligent Information Systems 6(2/3), 223–259 (1996)
Fensel, D.: Ontologies: A silver bullet for knowledge management and electronic commerce. Springer, Heidelberg (1998)
Gaasterland, T.: Cooperative answering through controlled query relaxation. IEEE Expert 12(5), 48–59 (1997)
Godfrey, P.: Minimization in cooperative response to failing database queries. International Journal of Cooperative Information Systems 6(2), 95–149 (1993)
Greco, S., Matarazzo, B., Pappalardo, N., Slowinski, R.: Measuring expected effects of interventions based on decision rules, Special Issue on Knowledge Discovery. Journal of Experimental and Theoretical Artificial Intelligence 17(1–2), 103–118 (2005)
Liu, B., Hsu, W., Chen, S.: Using general impressions to analyze discovered classification rules. In: Proceedings of KDD 1997 Conference, Newport Beach, CA. AAAI Press, Menlo Park (1997)
Pawlak, Z.: Rough Ssets and decision tables. LNCS, pp. 186–196. Springer, Heidelberg (1985)
Pawlak, Z.: Rough Sets: Theoretical aspects of reasoning about data. Kluwer Academic Publisher, Dordrecht (1991)
Raś, Z.W., Dardzińska, A.: Handling semantic inconsistencies in distributed knowledge systems using ontologies. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS, vol. 2366, pp. 66–74. Springer, Heidelberg (2002)
Raś, Z.W., Wieczorkowska, A.: Action-rules: How to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)
Raś, Z.W., Tzacheva, A., Tsay, L.-S.: Action rules. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 1–5. Idea Group Inc., USA (2005)
Silberschatz, A., Tuzhilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of KDD 1995 Conference. AAAI Press, Menlo Park (1995)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 5(6) (1996)
Raś, Z., Gupta, S.: Global action rules in distributed knowledge systems. Fundamenta Informaticae Journal 51(1-2), 175–184 (2002)
Skowron, A., Grzymala-Busse, J.: From the Rough Set Theory to the Evidence Theory. ICS Research Reports, 8/91, Warsaw University of Technology (October 1991)
Sowa, J.F.: Ontology, Metadata and Semiotics. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS (LNAI), vol. 1867, pp. 55–81. Springer, Heidelberg (2000)
Suzuki, E., Kodratoff, Y.: Discovery of surprising exception rules based on intensity of implication. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, Springer, Heidelberg (1998)
Tsay, L.-S., Raś, Z.W., Dardzińska, A.: Mining E-Action Rules. In: Mining Complex Data. In: Proceedings of 2005 IEEE ICDM Workshop in Houston, pp. 85–90. Published by Math. Dept., Saint Mary’s Univ., Texas, Nova Scotia, (2005)
Tsay, L.-S., Raś, Z.W.: Action rules discovery: System DEAR2, method and experiments. Special Issue on Knowledge Discovery, Journal of Experimental and Theoretical Artificial Intelligence 17(1-2), 119–128 (2005)
Tzacheva, A., Raś, Z.W.: Action rules mining. Special Issue on Knowledge Discovery, International Journal of Intelligent Systems, 20(7), 719–736 (2005)
Zbidi, N., Faiz, S., Limam, M.: On mining summaries by objective measures of interestingness. Machine Learning 62(3), 175–198 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dardzińska, A., Raś, Z.W. (2006). Cooperative Discovery of Interesting Action Rules. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_41
Download citation
DOI: https://doi.org/10.1007/11766254_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34638-8
Online ISBN: 978-3-540-34639-5
eBook Packages: Computer ScienceComputer Science (R0)