Action Rules Mining Triggered by Micro-actions and Its Application in Education

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. This paper gives a new approach for generating action rules by incorporating a pruning step through micro-actions. The notion of Micro-actions is introduced. They are nodes in a higher-level knowledge, which are linked with atomic terms showing changes within classification attributes. New influence matrix is presented and used to show the cascading effect of actions modeled as action rules. Moreover, an application of the proposed approach in education is demonstrated.

[1]  Dieter Fensel,et al.  Ontologies: A silver bullet for knowledge management and electronic commerce , 2002 .

[2]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[3]  Zbigniew W. Ras,et al.  Constraint Based Action Rule Discovery with Single Classification Rules , 2007, RSFDGrC.

[4]  Tsau Young Lin,et al.  Foundations and Novel Approaches in Data Mining , 2006, Studies in Computational Intelligence.

[5]  Zengyou He,et al.  Mining action rules from scratch , 2005, Expert Syst. Appl..

[6]  Zbigniew W. Ras,et al.  ARAS: Action Rules Discovery Based on Agglomerative Strategy , 2007, MCD.

[7]  Salvatore Greco,et al.  Measuring expected effects of interventions based on decision rules , 2005, J. Exp. Theor. Artif. Intell..

[8]  Xiang Li,et al.  Developing event-condition-action rules in real-time active database , 2007, SAC '07.

[9]  Zdzislaw Pawlak,et al.  Information systems theoretical foundations , 1981, Inf. Syst..

[10]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[11]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[12]  Zbigniew W. Ras,et al.  Action Rules Discovery without Pre-existing Classification Rules , 2008, RSCTC.

[13]  Zbigniew W. Ras,et al.  Mining for interesting action rules , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[14]  Zbigniew W. Ras,et al.  Action-Rules: How to Increase Profit of a Company , 2000, PKDD.

[15]  Zbigniew W. Ras,et al.  Association Action Rules , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[16]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[17]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[18]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

[19]  Zbigniew W. Ras,et al.  Action Rules Discovery, a New Simplified Strategy , 2006, ISMIS.

[20]  Djamel A. Zighed,et al.  Mining Complex Data, ECML/PKDD 2007 Third International Workshop, MCD 2007, Warsaw, Poland, September 17-21, 2007, Revised Selected Papers , 2008, MCD.

[21]  Zbigniew W. Ras,et al.  Action Rule Extraction from a Decision Table: ARED , 2008, ISMIS.

[22]  Ke Wang,et al.  Mining Actionable Patterns by Role Models , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[23]  Zbigniew W. Ras,et al.  Extracting Rules from Incomplete Decision Systems: System ERID , 2006, Foundations and Novel Approaches in Data Mining.

[24]  Hisham M. Haddad,et al.  Proceedings of the 2007 ACM Symposium on Applied Computing (SAC), Seoul, Korea, March 11-15, 2007 , 2007, SAC.

[25]  Zbigniew W. Ras,et al.  Action Rules Discovery System DEAR_3 , 2006, ISMIS.