## 3 Results
### Identifying the Data Pattern and Identifying the Fault Cause  
To divide the data into meaningful categories, it was necessary to apply different clustering algorithms to the data, according to the examined scenarios and the results obtained from the evaluation of the clusters. The best separating algorithm should be identified from the drive test data. Since the data of this part was similar to the data of the last section, it was raw data, and it was impossible to use external criteria to evaluate the correctness of the obtained results. It was necessary to use internal performance evaluation criteria to assess the obtained clusters. The standard for evaluating the clustering of the drive test data was also similar to the performance support system data, namely, the Silhouette coefficient. It should be noted that to ensure the ability to cluster the data, the Hopkins coefficient was applied to the data of each scenario. The obtained result for the scenario of the idle mode was 0.88%, and for the long call it was 0.94%. Therefore, the data had good capability for clustering. The analysis of the obtained results, according to different scenarios, is given in the following part. Table 5 summarizes the related information to the features examined in this test. Scenario number 1, Idle mode: This tests the received signal level at different points of the network. Locations with inadequate signal levels are reported to the optimizer for further investigation. According to the characteristics that were considered in this scenario, including the signal level, the ratio of the carrier power to the interference and the advanced scheduler, and the number of optimal clusters, which were of three numbers, according to the expert's opinion, after data pre-processing, we had the results indicated in Figure 14.  
Figure 14: Silhouette coefficient on the idle mode scenario with the number of 3 clusters. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature’s Name** & **Minimum** & **Maximum** & **Optimal Interval (Standard)** \\ \hline Carrier-to-interference ratio & 5 & 25 & \(\geq\)25, \(<\)35 \\ \hline Signal level & \(-\)101 & \(-\)31 & More than \(-\)65 \\ \hline Signal quality & 0 & 7 & Less than 3 \\ \hline Call quality & 0 & 4.1 & Equal to 3.9 \\ \hline Advanced timing & 0 & 3 & Less than 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of the statistical information of the used features. Therefore, on the drive test data in the idle state scenario, the estimation maximization (EM) method provided a more suitable answer than other methods. A more complete description of the obtained clusters' characteristics is given in Table 6.  
Each of the obtained clusters indicated good, average and unfavorable network conditions in terms of the received signal characteristics of the mobile phone in the idle mode. In cluster number 1, according to the statistical information table of the used features, all records were in a suitable and optimal condition. In cluster number 2, although the inter \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature’s Name** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline Signal level & 15 & 10 & 5 \\ Interference ratio & 10 & 5 \\ \hline Advanced timing & 10 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The details of Clusters and Feature’s name. ference in the environment was negligible, the signal level was weaker than the optimal mode. The low signal level in the records included in this category was due to the relatively large distance between the base transmitter and receiver station. Since there was very little interference in this category, these areas did not have the problem of improper frequency design of adjacent cells or the problem of interfering signals from neighbors. Therefore, the records of this cluster did not report a specific problem in the network. If a new antenna was installed to improve the signal level in this area, it would interfere with the received signal in the neighboring points.