基于领域知识的冗余关联规则消除算法

Elimination algorithm of redundant rules in association rules mining based on domain knowledge

关联规则挖掘算法用于从大型数据库中提取感兴趣的规则,然而,在领域知识中已经能清晰表示的知识并没有被充分考虑,关联规则挖掘算法提取的规则中包含了大量已知的关联性,从而产生了很多冗余规则。文章提出一种算法DKARM,同时考虑了数据本身以及相关的领域知识,以消除在领域知识中清晰表示的已知关联性。实验表明,该算法合理消除了冗余规则,有效降低了规则数目。

Many association rule mining algorithms have been developed to extract interesting patterns from large databases.However,a large amount of knowledge explicitly represented in domain knowledge（DK） has not been used to reduce the number of association rules.A significant number of well known dependences are unnecessarily extracted by association rule mining algorithms,which results in the generation of hundreds or thousands of non-interesting association rules.This paper presents a DKARM algorithm,which takes both database and relative DK into account,to eliminate all associations explicitly represented in DK.Experiments on the proposed algorithm show the significant reduction of the number of rules and the elimination of non-interesting rules.