基于规则模式LRU优化的交互式关联规则挖掘

Interactive Association Rule Mining Based on Rule Schema LRU Optimization

将规则模式应用到交互式关联规则挖掘算法中,能有效提高用户的参与程度,然而如果规则模式数目较多,需要花费大量精力处理规则模式及其模式级别。通过LRU算法优化规则模式,提出IAFBLRU算法。在该算法中,用户可主动提高感兴趣的规则模式的级别;也可采用LRU策略自动降低用户不感兴趣规则的模式级别。实验证明IAFBLRU算法有效提高了关联规则挖掘算法的质量。

Applying rule schemas to interactive association rule mining can improve the participation of users effectively;however if the schemas are larger,much effort has to be spend on rule schemas and their levels.LRU is introduced to optimize rule schemas,and IAFBLRU is proposed in this paper,in which users can proactively improve high interesting rule schemas＇ level;and also LRU policy can reduce the uninterested rule schemas＇ level automatically.Experiments show that the algorithm improves the quality of association rule mining algorithm effectively.