  Key performance indicators(KPIs) are of great significance in the monitoring
of wireless network service quality. The network service quality can be
improved by adjusting relevant configuration parameters(CPs) of the base
station. However, there are numerous CPs and different cells may affect each
other, which bring great challenges to the association analysis of wireless
network data. In this paper, we propose an adjustable multi-level association
rule mining framework, which can quantitatively mine association rules at each
level with environmental information, including engineering parameters and
performance management(PMs), and it has interpretability at each level.
Specifically, We first cluster similar cells, then quantify KPIs and CPs, and
integrate expert knowledge into the association rule mining model, which
improve the robustness of the model. The experimental results in real world
dataset prove the effectiveness of our method.
