Communication Network Alarm Correlation Based on Multi-dimensional Fuzzy Association Rules Mining

Abstract

Fault diagnosis is a challenging work in large communication network. A fault will bring more than one alarms, an alarm may be caused by different faults vice versa. The relationship between faults and alarms are not accurate but fuzzy, which can not be understood using traditional Boolean logic. Also, crisp association rules use sharp partitioning and potentially introduce loss of information. Therefore, fuzzy logic is used to convert numerical attributes into linguistic terms and a fuzzy judge language set is described. We first analyze the meanings of each attribute of network alarms. Then define proper fuzzy membership functions according to the relationship between this field and the root alarm. After that we integrate MDs (membership degrees) and weights of every attribute to construct fuzzy database. At last, we come up with a new fuzzy association rules mining algorithm, which generalizes the popular hierarchy-based algorithm. Simulation experiments on a synthetic network are carried out to prove and validate the accuracy and efficiency of our algorithm with respect to correlation analysis for right positioning root alarms.