基于改进Apriori关联规则算法的信令分析

Signaling Analysis Based on Improved Apriori Association Rule Algorithm

传统信令分析方法需要专业人员找出可能与失败码相关的聚集的信令字段值或值组合及其导致失败的概率,并定位网络问题,操作复杂且效率低。通过改进Apriori关联规则算法,将探寻聚集的字段值或其组合的过程转换成发现失败码和相关信令字段值的关联规则。在计算频繁项集时,通过设置最小支持度阈值找出包含失败码的频繁项,将待分析失败码作为后项,减少了算法的复杂度和算力要求,并通过置信度和提升度找出与后项强关联的属性,实现了对失败码集中属性的快速高效识别。

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.