摘要
考虑属性数量和属性权值对关联规则的影响,提出一种新的加权支持度和加权置信度计算方法,在挖掘加权关联规则时通过改进加权支持度设置模型保持Apriori算法的频繁集向下封闭特性。与Apriori算法和水平加权关联规则挖掘方法的比较结果证明该方法能快速有效地挖掘重要的关联规则。
Considering the impact of the property number and weight on association rules, this paper presents a new method for mining weighted association rules, which can hold the downward closed property by using an improved model of weighted support measurements when mining association rules. Compared with Apriori algorithm and horizontal weighted association rules mining method, it proves that the method can quickly and efficiently mine important association rules.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第7期55-57,共3页
Computer Engineering
关键词
加权关联规则
加权支持度
频繁项集
weighted association rule
weighted support
frequent itemset
