Data clustering for network traffic modeling

Abstract

The described technology is generally directed towards data clustering for network traffic modeling. Cellular network measurement data from different geographic areas can be separated into clusters based on similarities in network performance indicators, cell traffic load data, their changing pattern over time, and/or other metrics. A machine learning model can then be assigned to each cluster, and the machine learning models can be trained to make network traffic control decisions under conditions exhibited in their respective clusters. If the error rate of the trained machine learning models is acceptable, then the machine learning models can be deployed for use at network equipment. If the overall error rate is not acceptable, then the cellular network measurement data can be re-separated into a larger number of clusters, and machine learning models can again be trained for each cluster. The re-separation of data and re-training of machine learning models can repeat until the error rate is acceptable and the machine learning models can be deployed.