Novel alarm correlation analysis system based on association rules mining in telecommunication networks

Alarm correlation analysis system is an useful method and tool for analyzing alarms and finding the root cause of faults in telecommunication networks. Recently, the application of association rules mining becomes an important research area in alarm correlation analysis. In this paper, we propose a novel Association Rules Mining based Alarm Correlation Analysis System (ARM-ACAS) to find interesting association rules between alarm events. In order to mine some infrequent but important items, ARM-ACAS first uses neural network to classify the alarms with different levels. In addition, ARM-ACAS also exploits an optimization technique with the weighted frequent pattern tree structure to improve the mining efficiency. The system is both efficient and practical in discovering significant relationships of alarms as illustrated by experiments performed on simulated and real-world datasets.