摘要
本文提出了一种通用的增量式关联规则挖掘算法MIAR,可用于数据库更新改变时的挖掘.研究并提出了增量式关联规则挖掘中的重要性质,充分利用上一次挖掘出的知识,对候选项集进行修剪.确定了一种启发式的数据库选择扫描策略,在保证候选项集数不会增长很快的情况下,减少数据库扫描次数,有效提高算法的时间性能.大量数据试验算法优越于Apriori和FUP2.
An incremental updating algorithm technique for mining association rule-MIAR is developed and applied in the database mining.The research shows Fast Update(FUP) algorithm always produces a lot of candidate itemsets and scans database many times.An improved Pruning and FastUpdating(PFUP) algorithm is presented to solve such two bottleneck problems of FUP.PFUP algorithm joins strong large itemsets into smallquantitative of candidate itemsets based on strong large itemsets concept,and adopts early pruning strategy to cut down the times of scanning database.Test result shows that the performance efficiency of new algorithm is obviously better than Apriori and FUP2 algorithm.
出处
《青海师范大学学报（自然科学版）》
2009年第3期39-42,共4页
Journal of Qinghai Normal University(Natural Science Edition)
关键词
改进式增量式关联规则
挖掘算法技巧
MIAR
Incremental updating algorithm
technique for mining association rules
MIAR
