An Adaptive Adjustment Algorithm of the Parameters in Alarm Association Rule Mining

Xiaodan Shi, Libin Jiao, Gang Cheng, Yang Yang, Xingyu Chen, Peng Yu

Abstract: With the rapid development of communication technology, communication networks are playing an increasingly important role in people's lives. Effective management of increasingly complex networks can improve the efficiency and stability of network operations. Fault management is one of the important functions of network management. The analysis of the alarms generated in the network can dig into the underlying rules to provide useful information for fault management. However, the existing alarm association rule mining algorithms often have the problem of parameter rigidity. In this paper, a two-level windows based alarm transaction extracting algorithm is proposed to solve the problem of low efficiency when using fixed size windows. Then this paper proposes an experience extraction method based on alarm priority in deep Q network (DQN), which can calculate the sampling probability according to the importance of alarm when the memory unit enters the queue. Aiming at the rare item problem caused by the fixed support threshold in association rule mining, the improved DQN is used to dynamically adjust the minimum support in rule mining algorithm. Experimental results show that the algorithm proposed in this paper can effectively improve the efficiency of alarm transaction extraction and the accuracy of alarm association rules mining.