MAROR: Multi-Level Abstraction of Association Rule Using Ontology and Rule Schema

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Salim Khiat 1,* Hafida Belbachir 1 Sid Ahmed Rahal 1

1. University of Sciences and Technology-Mohamed Boudiaf (USTOMB)/ Computer Sciences and Mathematics Faculty/ Computer Sciences Department Oran, 31000, Algeria

* Corresponding author.


Received: 24 Apr. 2014 / Revised: 3 Aug. 2014 / Accepted: 21 Sep. 2014 / Published: 8 Nov. 2014

Index Terms

Multi-Level Rule Synthesis, Local Pattern Analysis, Global Rules, Exceptional Rules, Rule Schema


Many large organizations have multiple databases distributed over different branches. Number of such organizations is increasing over time. Thus, it is necessary to study data mining on multiple databases. Most multi-databases mining (MDBM) algorithms for association rules typically represent input patterns at a single level of abstraction. However, in many applications of association rules – e.g., Industrial discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to set of beliefs (and representational) commitments regarding the domain of interest. Using domain ontologies, we strengthen the integration of user knowledge in the mining and post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task at different levels. This paper formalizes the problem of association rules using ontologies in multi-database mining, describes an ontology-driven association rules algorithm to discoverer rules at multiple levels of abstraction and presents preliminary results in petroleum field to demonstrate the feasibility and applicability of this proposed approach.

Cite This Paper

Salim Khiat, Hafida Belbachir, Sid Ahmed Rahal, "MAROR: Multi-Level Abstraction of Association Rule Using Ontology and Rule Schema", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.12, pp.24-34, 2014. DOI:10.5815/ijitcs.2014.12.04


[1]Ramachandrarao Pralhad, Animesh Adhikari, Witold Pedrycz,« Developing Multi-Database Mining Applications », Advanced Information and Knowledge Processing; Springer-Verlag London Limited.2010 

[2]Ramkumar Thirunavukkarasu, Srinivasan Rengaramanujam; «Modified Algorithms for Synthesizing High-Frequency Rules from Different Data Sources», Knowl Inf Syst 17:313–334. (2008) Springer 

[3]Ramkumar Thirunavukkarasu, Srinivasan Rengaramanujam; «Multi-Level Synthesis of Frequent Rules from Different Data-Sources», International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2010 

[4]Zhang Chengqi, Meiling Liu, Wenlong Nie, and Shichao Zhang, «Identifying Global Exceptional Patterns in Multi-database Mining », IEEE Computational Intelligence Bulletin Feburuary 2004 Vol.3 No.1. 

[5]Zhang Shichao, Chengqi Zhang, Jeffrey Xu Yu, «An Efficient Strategy for Mining Exceptions in Multi-Databases ». Article in press; An international journal information Science. Elsevier.2003

[6]Xindong Wu, Shichao Zhang, Chengqi Zhang; « Multi-Database Mining » IEEE Computational Intelligence Bulletin Vol.2 No.1 2003.

[7]Nigro H.O., S.E Gonzalez Cisaro, and Xodo: « Data Mining With Ontologies: Implementations, Findings and Frameworks». Idea Group Reference. 

[8]Marinica Claudia « Association Rule Interactive Post-processing using Rule Schemas and Ontologies – ARIPSO» These de doctorat en philosophy de "Ecole poltechnique de l'Universit_e de Nantes" Department d’informatique. 26 October 2010

[9]Srikant.R and Agrawal.R. «Mining Generalized Association Rules». Proceedings of the 21st International Conference on Very Large Databases, pages 407–419. Zurich, Swizerland 1995.

[10]Češpivová.H, J. Rauch, V. Svátek, M. Kejkula, and M. Tomečková. «Roles of Medical Ontology in Association Mining CRISP-DM Cycle». Workshop Knowledge Discovery and Ontologies in ECML/PKDD.(2004)

[11]Euler.T, and Scholz.M. (2004) Using Ontologies in a KDD Workbench». In Workshop on Knowledge Discovery and Ontologies at ECML/PKDD. 

[12]Charest M, Delisle S. «Ontology-Guided Intelligent Data Mining Assistance:Combining Declarative and Procedural knowledge». In Artificial Intelligence and. Soft Comput 2006:9–14

[13]Zhou.X and Geller.J. (2007). «Raising, to Enhance Rule Mining in Web Marketing with the Use of an Ontology». Date Mining with Ontologies: Implementations, Findings and Frameworks, pages 18-36. 

[14]Zadeh L.A, «Fuzzy Sets », In: Fuzzy Sets and Applications: Select Papers by L.A. Zadeh, Edited by R. R. Yager; S. Ovchinnikov, et al, Wiley-Interscience, 29-44.1987

[15]Escovar Eduardo L.G, M.Biajiz, and M.T.P. Vieira. «SSDM: A Semantically Similar Data Mining Algorithm». In XX Simposio Brasileiro de Banco de Dados (SBBD), pages 265–279, Uberlândia, MG, Brasil(2005).

[16]Escovar Eduardo L.G., Yaguinuma, C.A., Biajiz, M. «Using Fuzzy Ontologies to Extend Semantically Similar Data Mining». In: 21st Brazilian Symposium of Databases, Florianópolis, Brazil, October 16-20 (2006)

[17]Miani Rafael Garcia, Cristiane A. Yaguinuma, Marilde T.P. Santos, and Mauro Biajiz «NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies». Springer-Verlag Berlin Heidelberg pp. 415–426,2009.

[18]Mansingh Gunjan, Kweku-Muata Osei-Bryson and Han Reichgelt « Using Ontologies to Facilitate Post-Processing of Association Rules by Domain Experts » Information Sciences 181 (2011) 419-434 ELSEVIER.

[19]Manda.P, McCarthy F, Bridges S.« Interestingness measures and Strategies for Mining Multi-Ontology Multi-Level Association Rules from Gene Ontology Annotations for the Discovery of New GO Relationships». Journal of Biomedical Informatics: Available online 11 July 2013 - DOI:10.1016/J.JBI.2013. 06. 012 

[20]Neves Inhaúma Ferraz1 and Ana Cristina Bicharra Garcia: «Ontology in Association Rules». SpringerPlus 2013.

[21]Liu Bing, Wynne Hsu, Ke Wang and Shu Chen, 1999: « Visually Aided Exploration of Interesting Association Rules». Proceeding of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining. Lecture Notes In computer science, Vol, 1574, Springer-Verlag, pages 26-28. 

[22]Gruber. T.R: « A translation Approach to Portable Ontology Specification» Knowledge acquisition, 5(2):199-220 (1993).

[23]Sowa.J: «Knowledge Representation». Logical, philosophical, and Computational Foundations. Books Cole Publishing Co..Pacific grove, CA (2000)

[24]Zhang Shichao, Chengqi Zhang, Jeffrey Xu Yu, «Identifying Interesting Patterns in Multi-Databases ». Studies in Computational Intelligence (SCI) 4.91-112. Springer-Verlag Berlin Heidelberg 2005.

[25]Agrawal Rakesh, Ramakrishnan Srikan « Fast Algorithms for Mining Association Rules » In VLDB’94 pp 487-499.