Alarm Association Algorithms Based on Spectral Graph Theory

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 alarms. This paper proposes a new mining algorithm based on spectral graph theory. The algorithms firstly sets up alarm association model with time series; Secondly, it regards alarms database as a high-dimensional structure and treats alarms with associated characteristics as part of it. The algorithm discovers the underlying mapping low-dimensional structure embedding in high-dimensional space based on spectral graph theory. Experimental results based on synthetic and real datasets demonstrates that this algorithm not only discoveries association among alarms, but also acquires the fault in the telecommunications network based on the spectral graph transformation.