MRAR : mining multi-relation association rules

Ramezani, R; Saraee, MH; Nematbakhsh, MA

Abstract

In this paper, we introduce a new class of association rules (ARs) named
“Multi-Relation Association Rules” which in contrast to primitive ARs (that
are usually extracted from multi-relational databases), each rule item consists
of one entity and several relations. These relations indicate indirect relationship
between entities. Consider the following Multi-Relation Association Rule where
the first item consists of three relations live in, nearby and humid: “Those who
live in a place which is near by a city with humid climate type and also are
younger than 20 their health condition is good”. A new algorithm called
MRAR is proposed to extract such rules from directed graphs with labeled
edges which are constructed from RDBMSs or semantic web data. Also, the
question “how to convert RDBMS data or semantic web data to a directed graph
with labeled edges?” is answered. In order to evaluate the proposed algorithm,
some experiments are performed on a sample dataset and also a real-world drug
semantic web dataset. Obtained results confirm the ability of the proposed
algorithm in mining Multi-Relation Association Rules.

Citation

Ramezani, R., Saraee, M., & Nematbakhsh, M. (2014). MRAR : mining multi-relation association rules. Journal of computing and security (Online), 1(2), 133-158

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2014
• Online Publication Date: Sep 13, 2014
• Publication Date: Sep 13, 2014
• Deposit Date: Jun 5, 2017
• Publicly Available Date: Jun 5, 2017
• Journal: Journal of Computing and Security
• Print ISSN: 2322-4460
• Electronic ISSN: 2383-0417
• Volume: 1
• Issue: 2
• Pages: 133-158
• Publisher URL: http://www.jcomsec.org/index.php/JCS/article/view/84
• Related Public URLs: http://www.jcomsec.org