Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

Cellular network performance is often evaluated by key performance indicator (KPI) and key quality indicator (KQI). The association between KQI and KPI is the most critical step to optimize the performance of cellular network. Traditional association methods between KPI and KQI are based on the end-...

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Bibliographic Details
Main Authors: Guanghui Fan, Wenjuan Shi, Liang Guo, Jun Zeng, Kaixuan Zhang, Guan Gui
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Quantitative association rule mining (QARM)
machine learning
continuous attributes
sliding-window partioning (SWP)
random forest (RF)
Online Access:https://ieeexplore.ieee.org/document/8903320/

Internet

https://ieeexplore.ieee.org/document/8903320/

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