# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 4 Experiment and Discussion
### Experimental Results and Analysis
#### 4.2.2 Unlabeled Datasets  
Similarly, the clustering effect of this paper's model is compared with four classes of classical clustering methods, and the evaluation metrics are calculated based on the average of the 800 questions in the dataset. Table 3 shows the clustering results obtained using different clustering methods on the unlabeled dataset CodeNetPython800.  
Traditional clustering methods such as k-means [17], spectral clustering [18], and Meanshift [19], perform numerically lower for each evaluation metric than the deep clustering methods like DEC [21] and the proposed CMVAE model. This performance gap can be attributed to the fact that traditional clustering methods rely on predefined distance metrics in the original feature space. By contrast, deep clustering methods leverage neural networks to learn meaningful feature representations, enabling more effective clustering.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PaCon**} & \multicolumn{2}{c}{**Sorting-C\#**} & \multicolumn{2}{c}{**Sorting-Python**} \\ \cline{2-7}  & **Accuracy (\%)** & **FM Index** & **Accuracy (\%)** & **FM Index** & **Accuracy (\%)** & **FM Index** \\ \hline k-means [17] & 66.7 & 0.584 & 78.7 & 0.772 & 71.7 & 0.710 \\ Spectral Clustering [18] & 62.5 & 0.558 & 84.7 & 0.762 & 73.0 & 0.690 \\ Mean Shift Clustering [19] & 54.1 & 0.530 & 30.3 & 0.428 & 32.2 & 0.437 \\ DEC [21] & 75.8 & 0.703 & 83.3 & 0.777 & 74.3 & 0.733 \\ CMVAE & 82.4 & 0.793 & 93.9 & 0.876 & 91.4 & 0.854 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different clustering methods on labeled datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Silhouette Coefficient** & **CH Index** & **DB Index** \\ \hline k-means [17] & 0.263 & 81.682 & 1.559 \\ Spectral Clustering [18] & 0.191 & 72.706 & 1.469 \\ MeanShift [19] & 0.354 & 129.267 & 1.195 \\ DEC [21] & 0.262 & 485.004 & 1.415 \\ CMVAE & 0.390 & 593.795 & 1.118 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different clustering methods on the CodeNetPython800 dataset.  
Among the deep clustering methods, DEC [21] improves upon traditional approaches by jointly learning feature representations and cluster assignments. In comparison, our proposed CMVAE model incorporates a multi-view representation learning framework that employs a joint optimization strategy combining clustering loss and reconstruction loss from two autoencoders, which enhances the quality of learned representations and improves clustering performance. The CMVAE method achieves the highest values for contour coefficient (0.390), CH index (593.795), and DB index (1.118), indicating that the method is better able to classify the samples into tight and separated clusters.  
By comparing the clustering results of the different clustering methods, it is reflected that the CMVAE method based on multi-view learning proposed in this paper has a clear advantage in the clustering task, which can better capture the code's policy features and generate high-quality clustering results.