MRAR: Mining Multi-Relation Association Rules

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 rst 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 conrm the ability of the proposed algorithm in mining Multi-Relation Association Rules.