# Design and implementation for automated network troubleshooting using data mining

arXiv: https://arxiv.org/abs/1506.00108 · HTML: https://mlsanity.com/paper/1506.00108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2015.

## Built on by (most cited first, 1 of 1)

- [Efficient Online Decision Tree Learning with Active Feature Acquisition](https://mlsanity.com/paper/2305.02093/) (2023)

## Abstract (arXiv)

The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of fault isolation and localisation. The data mining techniques described in this paper are intended to allow a system to be trained to actually learn network fault rules. The results of the tests that were conducted allowed for the conclusion that the rules were highly effective to improve network troubleshooting.