北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2007, Vol. 30 ›› Issue (1): 66-70.doi: 10.13190/jbupt.200701.66.xuqf

• 论文 • 上一篇    下一篇

一种基于相关度统计的告警关联规则挖掘算法

徐前方1, 肖 波2 ,郭 军1   

  1. 1. 北京邮电大学 信息工程学院, 北京 100876; 2. 北京邮电大学 电信工程学院, 北京 100876
  • 收稿日期:2006-02-12 修回日期:1900-01-01 出版日期:2007-03-30 发布日期:2007-03-30
  • 通讯作者: 徐前方

An Mining Algorithm with Alarm Association Rules Based on Statistical Correlation

XU Qianfang1 , XIAO Bo2 , GUO Jun1   

  1. 1. School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876,China;
    2. School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876,China
  • Received:2006-02-12 Revised:1900-01-01 Online:2007-03-30 Published:2007-03-30
  • Contact: XU Qianfang

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.

关键词: 故障管理, 关联规则, 数据挖掘, 相关度

Abstract:

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 low-frequency.

Key words: fault management, association rules, data mining, correlation

中图分类号: