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.Comment: 19 pages, 7 figures, International Journal of Data Mining & Knowledge
Management Process (IJDKP) Vol.5, No.3, May 201
