Multi-Level Association Rule Mining for Wireless Network Time Series Data
This work addresses the problem of optimizing wireless network service quality for network operators by enabling more effective association analysis between configuration parameters and key performance indicators, though it appears incremental in its approach.
The paper tackles the challenge of analyzing associations in wireless network data by proposing an adjustable multi-level association rule mining framework that integrates expert knowledge, improving model robustness and interpretability, with experimental results on real-world datasets proving its effectiveness.
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.