# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 5 Experimental Evaluation
### Results and Discussion  
In this section, we present the results of our experiments and discuss our research questions. Table 4 shows the overall results of our experiments that we executed as we described in the previous subsections, noting that we performed 10-fold cross-validation on the training and validation data in our experiments. The following is a discussion of each research question with more details. To answer RQ1, we used four different metrics to evaluate the effectiveness of GWO-optimized SOM in comparison with K-Means clustering and traditional SOM. These metrics are the F1-score, precision, recall, and accuracy. As can be seen in Table 4, each of the clustering techniques is evaluated for both datasets, MSA and NADA, and also with both representation methods. We also show our results in comparison with the K-Means clustering implementation in [19]. As highlighted in the table, optimizing the SOM using the GWO algorithm resulted in improving the clustering for both datasets with both representation methods. The results show a consistent improvement in all the effectiveness metrics we used in our evaluation, indicating the effectiveness of the introduced model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Representation** & **Clustering Technique** & **F1-Score** & **Precision** & **Recall** & **Accuracy** & **Time in Minutes** \\ \hline \multirow{8}{*}{MSA} & CV & K-Means & 73.00\% & 75.00\% & 89.60\% & 73.00\% & - \\  & TF-IDF & K-Means & 90.80\% & 89.80\% & 94.20\% & 93.80\% & - \\  & K-Means in [19] & & 87.32\% & 87.13\% & 87.52\% & - & - \\  & CV & SOM & 86.59\% & 87.06\% & 86.66\% & 86.70\% & 07:18 \\  & TF-IDF & SOM & 93.37\% & 93.54\% & 93.33\% & 93.30\% & 06:27 \\  & CV & SOM + GWO & 98.14\% & 98.21\% & 98.14\% & 98.00\% & 07:00 \\  & TF-IDF & SOM + GWO & 98.33\% & 98.36\% & 98.33\% & 98.30\% & 04:48 \\ \hline \multirow{8}{*}{NADA} & CV & K-Means & 44.30\% & 34.30\% & 31.90\% & 44.30\% & - \\  & TF-IDF & K-Means & 57.70\% & 60.20\% & 71.60\% & 57.70\% & - \\  & CV & SOM & 91.84\% & 92.01\% & 91.79\% & 91.80\% & 54:27 \\ \cline{1-1}  & TF-IDF & SOM & 93.33\% & 93.47\% & 93.29\% & 93.30\% & 18:22 \\ \cline{1-1}  & CV & SOM + GWO & 98.70\% & 98.77\% & 98.70\% & 98.70\% & 18:10 \\ \cline{1-1}  & TF-IDF & SOM + GWO & 98.44\% & 98.53\% & 98.42\% & 98.40\% & 16:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Final clustering results. The use of GWO for initializing the weights for SOM allows it to explore a larger space of possible initializations, increasing the chance of finding a more suitable starting point that leads to better final clustering results. To answer RQ2, we used the training time as a metric to evaluate the efficiency of GWO-optimized SOM in comparison with K-Means clustering and traditional SOM. As can be seen in Table 4, GWO-optimized SOM are faster than traditional SOM in training time for both the MSA and NADA datasets and in both the CV and TF-IDF data representations. We also noticed that the training time for all the clustering approaches is significantly higher in the NADA corpus when compared against the MSA corpus. This is mainly due to the larger size of the NADA corpus. After further investigation of the training time of the SOM and optimized SOM techniques, we found that the overhead of finding the best weights in the optimized SOM is high, but the SOM training time after initializing the weights is significantly reduced, resulting in an overall shorter training time compared to traditional SOM. As can be seen in Table 5, initializing the weights consists of about 30% of the overall running time for the GWO-optimized SOM. To answer RQ3, we compared the two representations, TF-IDF and CV, using the same metrics as in RQ1 and RQ2. As can be seen in Table 4, we observe that the TF-IDF representation technique reported better effectiveness for both datasets in all the evaluated metrics. However, in the GWO-optimized SOM clustering technique, the difference in the effectiveness results for the two representations was negligible.