Mobile traffic growth increases exponentially over the years. To gratify the growing traffic, which requires capacity and coverage, densification of a network is a key solution. As mobile network becomes larger and larger, it is difficult to manage the network manually rather it requires automated network management. Self-healing is one of self-organizing network (SON’s) functionalities that implements automatic fault management in radio access network (RAN). In practice, mobile cell outage is the major problem in the radio access network and leads to the lack of network service. The automated and timely detection of a malfunctioning cell is one of the crucial challenges for network operators. In this thesis, data mining model has been introduced to detect cell outage automatically. Density-based Local Outlier Factor (LOF) detection algorithm, which is a decisive part of the model, has been adopted and implemented using incoming handover statistical data to detect cell outage and sleeping cells in self-organizing manner. For this purpose, statistical handover data has been collected from real UMTS network and then preprocessed using filtering, aggregation, normalization and then profiling. Moreover, an improved version of LOF algorithm, fast anomaly detection with duplication (FADD), has also been implemented to improve the detection capability. Receiver Operating Characteristic (ROC) curve is used to show the degree of the performance of the algorithms. The study shows that the two versions of LOF cell outage detectors have detected most cells in outage and locate their positions. But, FADD has detected 89% compared to 75% of the original LOF.