Multi-Level Association Rule Mining for Wireless Network Time Series Data

TL;DR

This paper introduces a multi-level association rule mining framework for wireless network time series data, enabling interpretable analysis of KPIs and configuration parameters to enhance network service quality.

Contribution

It presents an adjustable, multi-level association rule mining method that incorporates environmental info and expert knowledge for robust wireless network analysis.

Findings

Effective in real-world datasets

Improves interpretability of association rules

Enhances network service quality management

Abstract

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.