## 4 Discussion and Conclusions
By implementing different algorithms with different numbers of clusters, 5 clusters with a Silhouette coefficient of 0.4509 for traffic data and 6 clusters with a Silhouette coefficient of 0.503 were obtained for signaling data. Each of these clusters represented a specific cause for a fault in the network. Finally, different classification algorithms were applied to the labeled data through clustering to better evaluate the results. For traffic and signaling data, combining the effects of different classifiers through opinion voting had the best accuracy in test data. In fact, 90.38% accuracy was obtained for traffic data and 92.31% for signaling data, which was a significant improvement compared to the accuracy of other similar tasks. Drive test data were collected in three short, long and idle mode call scenarios. The short call identifies network problems in call setup, the long call identifies issues related to handover and call interruption, and, finally, the idle mode ascertains characteristics of the standard signal in the network. This research used performance support system data to solve the problems of blocked calls in short calls, long calls, and idle mode and used clustering algorithms to identify the cause of existing problems. For the accuracy of this method, in addition to the Silhouette coefficient, various classified algorithms were performed on the training and test data in the case of long call scenarios. In the best case, an accuracy of 96.86% was obtained with the dynamic neural network method. In addition, the time window was used to provide a framework for identifying points that needed handover, and its results were presented in the third section of the outsourcing review section. As mentioned in the first section, the number of studies conducted in this field has been minimal, and this study can be expanded to various areas. Examining subscriber complaint data in more detail, including identifying the importance of the reported problem for operators, is one of the essential activities to reduce the time spent by experts in this industry. In addition, according to the record that the subscriber registers in the system and the explanations that they provide to the subscriber complaints center, it is possible to identify the fault type and analyze its cause. Conceptualization, A.K.S. ; Software, A.K.S., S.R. and A.J. ; Formal analysis, S.R., A.J., F.M., W.Z. and D.W.; Investigation, F.M. and W.Z. ; Resources, A.K.S., F.M. and D.W.; Data curation, A.K.S., S.R. and A.J. ; Writing--original draft, A.K.S., S.R. and A.J. ; Writing--review & editing, A.J. ; Visualization, A.J. ; Supervision, A.J. and W.Z. ; Project administration, A.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant No. HIT.OCEF.2021007, the Shenzhen Science and Technology Research and Development Foundation under Grant No.JCYJ20190806143418198, the National Key Research and Development Program of China under Grant No. 2020 YFB1406902, the Key-Area Research and Development Program of Guangdong Province under Grant No. 2020B0101360001, the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant No. 2022B1212010005. Professor [PERSON] is the corresponding author. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest.