Method based on rough set for mining multi-dimensional association rules
基于粗糙集的多维关联规则挖掘方法

It is very time-consuming to discover association rules from the mass of data, and not all the rules are attractive to the user, so 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, the authors defined a mining language which allowed users to specify items of interest to the association rules, as well as the parameters (for example, support, confidence, etc.). A method based on rough set theory for multi-dimensional association rules mining was also proposed, which dynamically generated frequent item sets and multi-dimensional association rules, and reduced the search space to generate frequent item sets. Finally, an example verifies the feasibility and effectiveness of the method.