Abstract: In the previous alarm correlation analysis algorithms, the alarm importance is regarded as the same. In order to distinguish the difference in importance of different alarms and the difference in the amount of information contained in the alarms, an alarm correlation analysis algorithm with improved weighting method is proposed. First, the attributes related to alarm importance in the alarm information are quantified, and the XGBoost(eXtreme Gradient Boosting) integrated learning model is used to train them to obtain the weight value of the alarm attribute, and the weight assigned to the alarm data. Then, the network topology data is added to the sliding window to improve the problems in the division of transactions by the sliding window. The improved transaction set divided by the sliding window is more realistic and reliable. Finally, the weighted alarm transaction set is used to mine frequent alarms and association rules by using the weighted FP-Growth(Frequent
Pattern Growth ) algorithm. Experiments show that the alarm correlation analysis algorithm with improved weighting method has good performance in mining frequent alarms, important association rules and time.

Key words: alarm correlation analysis, communications network, extreme gradient boosting ( XGBoost ) algorithm, weighted alarm analysis, frequent pattern growth(FP-Growth) algorithm