Multi-dimensional Association Rule Mining Algorithm

In this thesis we describe the state of art of data mining technology, and we study deeply one of the most important data mining tasks, i.e., mining of association rules. In the study of mining association rules, we focus on the mining of multidimensional association rules. After analyzing the basis approaches, we propose a novel algorithm named Multi-Apriori which combines data cube with Apriori efficiently using a FDPI-tree. Multi-Apriori can discover both inter-dimension association rules and hybrid-dimension association rules simultaneously, and has perfect performance on the whole.