摘要
挖掘告警序列间关联规则的算法都受到最小支持度的限制,仅能够得到频繁告警序列间的关联规则.对此,提出了一种以高相关度、高置信度为条件,通过聚类找到特征相同的网元告警群,然后基于相关度统计的挖掘算法.实验结果表明,该算法可以高效、准确地挖掘出电信网络告警数据库中频繁和非频繁告警序列间的关联规则.
Currently those algorithms to mine the alarm association rules are limited to the minimal support, so that they can only obtain the association rules among the frequently occurring alarm events, To address this problem, a new mining algorithm based on the statistical correlation was proposed, which firstly acquired the alarm net units with the same character by clustering; and then discovered the association rules from both high-frequency and low-frequency alarm events with the high correlativity and the high confidence. Experimental results demonstrated that this algorithm was efficient and accurate to mine the association rules among alarm events with both high-frequency and lowfrequency.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2007年第1期66-70,共5页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(60475007)
教育部跨世纪人才基金项目
关键词
故障管理
关联规则
数据挖掘
相关度
fault management
association rules
data mining
correlation
