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The efficient and effective monitoring of mobile networks is vital given thenumber 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 networksbased on the use of rules and decision tree data mining classifiers to upgradefault detection and handling. The goal is to have optimisation rules thatimprove anomaly detection. In addition, a monitoring scheme that relies onBayesian classifiers was also implemented for the purpose of fault isolationand localisation. The data mining techniques described in this paper areintended 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 thatthe rules were highly effective to improve network troubleshooting.

Design and implementation for automated network troubleshooting using data mining