Generalized Association Rule Mining Algorithms based on Data Cube

This paper defined a kind of multi-dimension data cube model, and presented a new formalization of generalized association rule based on data cube model. After comprehending the weaknesses of the current generalized association rule mining algorithms based on data cube, we proposed a new algorithm GenHibFreq which was suitable for mining multi-level frequent item set based on data cube. By taking advantage of the item taxonomy, algorithm GenHibFreq reduced the number of candidate itemsets counted, and had better efficiency. We also designed an algorithm GenerateLHSs-Rule for generating generalized association rule from multi-level frequent item set. Demonstrated through examples, algorithms proposed in this paper had better efficiency and less generated redundant rules than several existing mining algorithms, such as Cumulate, Stratify and ML_T2L1, and had good performance inflexibility, scalability and complexity and had new ideas on conducting the generalized association rule mining algorithms in multi-dimension environment and it also has great theoretical meaning and practical value.