基于综合相关置信度的告警关联规则挖掘

Mining Alarm Association Rules Based on Comprehensive Related Confidence

采用置信度融合算法计算相邻告警的综合相关置信度,并根据计算结果形成告警关联事务,增强告警事务内告警之间的相关性,降低对支持度的选择要求,有效挖掘出"低支持度-置信度"告警关联规则。利用h-置信度理论筛选有意义的告警关联规则,并通过实验比较了传统方法和本文方法的执行效率和挖掘正确率。

The traditional association rules mining algorithms preferred to applying single sliding time window, andthe mined alarm association rides were high support-high confidence. To solve this problem, a method was proposedto calculate comprehensive related confidence between the time adjacent items by the confidence fusion algorithm.Then generated alarm transactions were generated based on the calculated results, which enhanced alarm correlationwithin a transaction. The proposed approach can reduce the choice of support requirement and mine lowsupport-high confidence association rides effectively. Based on h-confidence theory, interesting alarm associationrules were screened. Experimental results showed the feasibility and effectiveness of this approach.