# Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features
## 5 Discussion
### Automatic Clustering Scheme of Situational Interest during Classroom Learning  
The classification was used to validate the cluster performed by the clustering for all the EEG features mentioned previously. The performance assessment parameters have been calculated in all the subtopics across all the channel positions. However, Table 8 only reported the classification model with the highest averaged value in all performance assessment parameters across the subtopic. Overall, both k-means and DBSCAN clustering gave their best performance with a minimum accuracy of 87% in clustering the students by their level of situational interest. Nevertheless, the k-means had performed better than the DBSCAN, since it had obtained the maximum value of one in all performance assessment parameters. The k-means was in its maximum capability to cluster the student's SI level when the mean delta and mean alpha power were used as the input. Based on Table 8, the performance of DBSCAN was slightly lower than the k-means. Even though studies reported that DBSCAN reached its highest accuracy of 99.2 \(\pm\) 0.7 in measuring stroke clinical outcomes [55], our findings presented the opposite. This might be due to our data being homogenous, and no outlier being detected (see Figures 8 and 9). Although DBSCAN had been found to give good performance in outlier and noise detection to classify dataset with outlier [56; 57], this algorithm had a poorer performance in our present study when compared to the k-means method. If we further compared the performance of the k-means algorithm across the electrode channel, the k-means achieved the highest accuracy at all the electrode positions in the mean delta power. In contrast to when the alpha power was used as the input, the results showed that the k-means obtained its highest performance only in the combination of F3 and F4 channels, but not in the single channels F3 and F4. Since the frontal lobe played a significant function in the recognition of interest, especially on an arithmetic task [32] and numerical operation [21; 22], the usage of combination channels F3 and F4 was better compared to the single channel used. However, the result from Wilcoxon signed-rank test showed that there was a significant difference between the three groups, only at the combination of F3 and F4. In terms of the classification model, the results showed that the model no. 23 (Subspace \(k\)NN) was the most efficient model to classify the cognitive attention reaction of situational interest features, since it had all the maximum value performance assessments when it was applied together with the combination of F3 and F4 channel and the k-means clustering. This model showed its highest efficiency in both mean delta and mean alpha power. To validate the effective reaction aspect of situational interest, both the k-means and DBSCAN algorithms achieved their maximum efficiency at single pair of prefrontal channels (FP1-FP2) and frontal channels (F3-F4). If we compared the performance of the algorithms in the combination of pairs, only the k-means achieved its maximum value in all performance parameters. Even though the DBSCAN had its maximum sensitivity, it was noticed that it had a lower accuracy, specificity, and precision compared to the k-means. A study from [PERSON] [58] revealed that greater left frontal asymmetry calculated from several frontal pairs was reliably related to approach learning. Thus, we proposed the average FAA on the combination pair was better compared to the FAA on a single pair. This selection was supported by the significant results from Wilcoxon signed-rank test (\(p\) = 0.0353). There were five classification models (model 10, 19, 22, 25, and 28) that gave the highest efficiency when evaluating the effective reaction of situational interest features, since it had all the maximum value performance assessments when it was applied together with the average FAA in the combination of prefrontal and frontal channels and the k-means clustering.