## 3 Results
### Clustering Analysis and Fault Cause Identification  
In this section, we examine the results obtained from the clustering performed on the performance support system data. In clustering, the data is generally divided into traffic and signaling data categories. The results of each type and their analysis are given in the following. First, the traffic clusters were checked, and then the signaling clusters were studied. As mentioned before, the characteristics used in the traffic section included:  
* The percentage of communication failure in the TCH channel.
* The percentage of congestion in the TCH channel.
* The percentage of loss in adopting the TCH channel.
* The average quality of the uplink and downlink signals.  
The clustering results of different methods are shown in Figure 10.  
As illustrated in the above figure, the Expectation-Maximization clustering method provided a better response to the data than other methods. In addition, the optimal number of clusters was 5. Therefore, the EM method was applied to the data with several 5 clusters. The information related to this clustering was provided to the expert, and the expert comments, confirming the correctness of the obtained clusters, are given below, along with a view of the data distribution in each cluster. The data detected as faults were divided into traffic and signaling categories, and, as mentioned, the optimal number of clusters for the traffic data under consideration was 5. Therefore, one of these clusters indicated a favorable situation in the traffic cluster, which meant that the problem was only in the signaling data, and the system had no problem in terms of traffic.  
Figure 9: A schematic of the data production tree with two fault and non-fault categories by the use of Quest algorithm.  
A similar situation existed for signaling data. The problem was that some records only had traffic issues (Figure 11). The allocated records in (c) suffered from high outliers in the SD channel and the capacity problem. SD channel congestion and outages are of great importance, due to their direct impact on the shared network experience. Some influential factors in increasing the absolute value of this channel are the coverage level, interference, low quality, and instability of communication links in the Abis interface. Since there was a congestion problem in this cluster, the probability of a coverage problem in this area was less than the probability of other reasons. On the other hand, the cut-off value in this cluster was much higher than the threshold. Therefore, the cause of the problem might be hardware problems. The congestion problem was very critical in (d). The behavior of the channel access success indicator was also unacceptable. Therefore, this cluster's record had the problem of inadequate coverage, as well as low signal strength. Furthermore, these sites should be checked for interference. Cluster (e) had the same problems as (d), in terms of high cut-off value and failure to access the channel, but the difference was that there was no congestion problem in this cluster. Finally, cluster (f) had a hardware problem, due to the wildly inappropriate success rate of reaching the signaling channel.  
Figure 11: Indicators of statistical distribution of the signaling sector. Each cluster represents a mode of the network.  
Figure 10: Silhouette coefficient according to the number of clusters for different clustering methods–traffic section.