论文标题
无线网络时间序列数据的多层关联规则挖掘
Multi-Level Association Rule Mining for Wireless Network Time Series Data
论文作者
论文摘要
关键性能指标（KPI）在监视无线网络服务质量方面具有重要意义。可以通过调整基站的相关配置参数（CPS）来提高网络服务质量。但是，有许多CP，不同的细胞可能会互相影响，这给无线网络数据的关联分析带来了巨大的挑战。在本文中，我们提出了一个可调节的多层关联规则挖掘框架，该框架可以在每个层面上定量地矿山关联与环境信息，包括工程参数和绩效管理（PMS），并且在每个级别都具有解释性。具体而言，我们首先聚集了相似的细胞，然后量化KPI和CP，并将专家知识整合到关联规则挖掘模型中，从而改善了模型的鲁棒性。现实世界数据集中的实验结果证明了我们方法的有效性。
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.
