基于粗糙集的多维关联规则挖掘方法

海量的数据使得关联规则挖掘非常耗时，而并非所有的规则都是用户感兴趣的，应用传统的挖掘方法会挖掘出许多无关信息。此外，目前大部分算法是针对单维规则的。因此，定义了一种挖掘语言使得用户可以指定感兴趣的项以及关联规则的参数（如支持度，置信度等），并提出一种基于粗糙集理论的多维关联规则挖掘方法，动态生成频繁集和多维关联规则，减少频繁项集的生成搜索空间。实例分析验证该算法的可行性与有效性。

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, 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.