A dynamic mining algorithm of association rules for alarm correlation in communication networks

Nowadays, communication network turns to be more complex, once there occurs a failure, there will result in multi-alarm-events which require relevant transactions. This paper describes the alarm correlation in communication networks based on data mining. The association rules from self-adaptation for dynamic network resource and service that build new rule by fully utilizing and maintaining rules formed before while transactions grow/delete or minimum support changes which enables the framework is easily updated and new discovery methods be readily incorporated within it. Both theoretical analysis and computer simulations illustrate outstanding performance of the proposed models, which can be further optimized by experiments for specific environment.