Association rule mining is an important research branch of data mining

and computing frequent itemsets or frequent predicate sets is the main problem.The paper aims at mining multidimensional association rules on a relational database which includes multi-value attributes

and studies a computing method for frequent predicate sets.It presents MPG algorithm and IMPG incremental algorithm.By constructing a frequent pattern graph and applying the depth-first-search method

MPG can find all frequent predicate sets and only scans database once.In addition

the method can be expanded into IMPG algorithm which is used for incremental association rules mining by increasing once database scan at most.The paper analyzes temporal and space performance of the algorithms

and proves their effectiveness by experiments.