概念格上无冗余关联规则的提取算法NARG

在数据挖掘中,关联规则是很有价值的一类规律。普通的挖掘算法会产生大量的规则,尤其是当最小支持度和最小可信度减少时,关联规则的数目急剧上升。如何对规则进行约减而又不丢失数据信息是消除冗余关联规则的关键。根据概念格的理论和冗余关联规则的性质,提出在概念格上提取无冗余关联规则的NARG算法。该算法可以得到最小的无冗余的关联规则集,而且不丢失任何信息,可有效提高关联规则生成的效率。

NARG Algorithm of Extracting Non-redundant Association Rule in Concept Lattice

Association rules are the very valuable kind of law in data mining. A large number of rules are usually generated from database using ordinary mining algorithms. Especially when the minimal support and minimal confidence are reduced, the number of association rules rise rapidly. The key of eliminating redundant association rules is to reduce rules without losing data information. This paper presents a new algorithm called NARG to extract non-redundant association rules based on concept lattice and properties of redundant association rules. This algorithm can gain the minimal non-redundant set of association rules while effectively improve efficiency of extracting rules without losing any information of data.