Research of Algorithms Based on a Frequent Pattern Graph for Mining Multidimensional Association Rules
Pan Jiu-hui · Dianzi xuebao · 2007
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-firstsearch 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.