Application of data mining technology in alarm analysis of communication network

Nowadays, with the continuous development of science and technology, the social demand for the network is more and more, and with the continuous expansion of the network scale, the complexity of the network is increasing day by day. How to locate the fault accurately and quickly from a large number of alarm information has become a problem. To solve this problem, we must use computer technology to study the automatic analysis technology of alarm correlation. Therefore, this paper applies data mining technology to communication network alarm analysis, and based on fuzzy theory, it can accurately describe the relationship between network alarm and fault reason, and combines data mining technology with fuzzy theory to form the network alarm correlation analysis method of fuzzy association rule mining. On this basis, in order to mine the fuzzy association rules of the fuzzy alarm database directly and avoid the interference in the process of converting the alarm database to the transaction database, this paper proposes a dynamic time window fuzzy association rule mining algorithm. Through the simulation analysis, compared with the traditional data mining technology, the fuzzy association rules mining method combined with the fuzzy theory has better performance, and the further proposed dynamic time window fuzzy association rules mining algorithm greatly improves the accuracy of the association rules confidence, and has good application performance in the network alarm correlation analysis.