Clustering Optimisation Techniques in Mobile Networks

TL;DR

This paper presents a data-driven approach combining clustering and classification techniques to optimize mobile network performance and fault detection, enhancing service quality for users.

Contribution

It introduces a novel combination of k-means clustering and decision tree classification for mobile network fault diagnosis and optimization.

Findings

Improved fault detection accuracy

Effective identification of network malfunction causes

Enhanced network performance monitoring

Abstract

The use of mobile phones has exploded over the past years,abundantly through the introduction of smartphones and the rapidly expanding use of mobile data. This has resulted in a spiraling problem of ensuring quality of service for users of mobile networks. Hence, mobile carriers and service providers need to determine how to prioritise expansion decisions and optimise network faults to ensure customer satisfaction and optimal network performance. To assist in that decision-making process, this research employs data mining classification of different Key Performance Indicator datasets to develop a monitoring scheme for mobile networks as a means of identifying the causes of network malfunctions. Then, the data are clustered to observe the characteristics of the technical areas with the use of k-means clustering. The data output is further trained with decision tree classification algorithms. The end result was that this method of network optimisation allowed for significantly improved fault detection performance