A Method Based on Rough Set for Mining Multi-dimensional Association Rules

It is very time-consuming to discover association rules from the mass of data, but not all the rules are interesting to the user, a lot of irrelevant information to the user´s requirements may be generated when traditional mining methods are applied. In addition, most of the existing algorithms are for discovering one-dimensional association rules. Therefore, this paper defines a mining language which allows users to specify items of interest to the association rules, as well as the criteria (for example, support, confidence, etc.), and proposes a method based on rough set theory for multi-dimensional association rule mining methods, dynamically generate frequent item sets and multi-dimensional association rules, which can reduce the search space to generate frequent itemsets. Finally, an example is used to illustrate the algorithm and verify its feasibility and effectiveness.