Modified adaptive resonance theory for alarm correlation based on distance hierarchy in mobile networks

Nowadays, a fault management incorporating with alarm correlation rules play an important role on network management and maintenance. Alarms are unsolicited messages received from network elements or network management systems carrying information about malfunction location, type, severity and other relevant information. Usually, alarm information consists of categorical and numerical attributes and is difficult to be processed by most of clustering algorithms. This paper introduces a method of mining correlation rules of alarm messages by modified adaptive resonance theory network (MART) algorithm. The experiments illustrate good performance of the proposed algorithm with concept hierarchy tree to solve similar degrees of mixed data. Furthermore, this method could be used to build a hybrid system with traditional case-based reasoning system.