An Algorithm for Mining Multidimensional Association Rules Using Boolean Matrix

In this paper an algorithm is proposed for mining multidimensional association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the items forming a rule come from different dimensions. It is an algorithm for mining multidimensional association rules from relational databases. The algorithm adopts Boolean relational calculus to discover frequent predicate sets. When using this algorithm first time, it scans the database once and will generate the association rules. A priori property is used in algorithm to prune the item sets. It is not necessary to scan the database again, it uses Boolean logical operations to generate the association rules.