1
Department of Computer Engineering, Ferdowsi University of Mashhad
2
Electrical & Computer Engineering, Isfahan University of Technology, Iran.
3
Department of Computer Engineering, University of Isfahan, Iran
Abstract
In this paper, we introduce a new class of association rules (ARs) named"Multi-Relation Association Rules" which in contrast to primitive ARs (thatare usually extracted from multi-relational databases), each rule item consistsof one entity and several relations. These relations indicate indirect relationshipbetween entities. Consider the following Multi-Relation Association Rule wherethe first item consists of three relations live in, nearby and humid: "Those wholive in a place which is near by a city with humid climate type and also areyounger than 20 → their health condition is good". A new algorithm calledMRAR is proposed to extract such rules from directed graphs with labelededges which are constructed from RDBMSs or semantic web data. Also, thequestion "how to convert RDBMS data or semantic web data to a directed graphwith labeled edges?" is answered. In order to evaluate the proposed algorithm,some experiments are performed on a sample dataset and also a real-world drugsemantic web dataset. Obtained results confirm the ability of the proposedalgorithm in mining Multi-Relation Association Rules.
Ramezani, R., Saraee, M., & Nematbakhsh, M. A. (2014). MRAR: Mining Multi-Relation Association Rules. Journal of Computing and Security, 1(2), 133-158.
MLA
Reza Ramezani; Mohamad Saraee; Mohammad Ali Nematbakhsh. "MRAR: Mining Multi-Relation Association Rules". Journal of Computing and Security, 1, 2, 2014, 133-158.
HARVARD
Ramezani, R., Saraee, M., Nematbakhsh, M. A. (2014). 'MRAR: Mining Multi-Relation Association Rules', Journal of Computing and Security, 1(2), pp. 133-158.
VANCOUVER
Ramezani, R., Saraee, M., Nematbakhsh, M. A. MRAR: Mining Multi-Relation Association Rules. Journal of Computing and Security, 2014; 1(2): 133-158.
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