A Visualization Algorithm for Alarm Association Mining
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, furthermore, the rules couldnt be visual display. This paper provides a novel mining alarm correlation visualization algorithm based on the non-linear reduced-feature mapping. The algorithm firstly projects the alarms on multidimensional space according to co-occurrence strength of the alarms, and then reduces the dimensions of the space, finally provides the relationship of the alarms to user with visualization. Experimental results based on synthetic and real datasets demonstrated that this algorithm not only discovered correlation among alarms, but also acquired the fault in the telecommunications network based on the graph transformation.
Fault management Alarm Correlation Data visualization
Xu Qianfang Li Chunguang Xiao Bo Guo Jun
Pattern Recognition and Intelligent System Laboratory,Beijing University of Posts and Telecommunications, Beijing
国际会议
北京
英文
326-330
2009-11-06（万方平台首次上网日期，不代表论文的发表时间）