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 couldn´t 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.