Signaling Analysis Based on Improved Apriori Association Rule Algorithm
Traditional signaling analysis methods require professionals to identify clustered signaling field values or combinations of values that may be related to failure codes and the probability of causing failure codes.Based on this,network problems can be located,which is complex and inefficient to operate.It improves the Apriori association rule algorithm to transform the process of exploring aggregated field values or their combinations into association rules for discovering failure codes and related signaling field values.When calculating the frequent itemset,the minimum support threshold is set to identify the frequent items containing failure codes.The failure codes to be analyzed are treated as the subsequent items,which reduces the complexity and computational power requirements of the algorithm,and the attributes strongly associated with the subsequent items are identified through confidence and enhancement,achieving fast and efficient identification of attributes in the failure code set.
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