  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
