# Soft Semi-Supervised Deep Learning-Based Clustering
## 4 Experiments
### Results and Discussion
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53 One can notice from Table 6 that the SC-DEC performance exceeded that of DL + k-means on all three datasets. Moreover, we can see that SC-DEC outperformed the non-constrained approach DEC [21] on the MNIST and STL-10 datasets. This proves the importance of the proposed semi-supervision information formulated as pairwise constraints in guiding the deep clustering process. Moreover, the results confirmed the importance of the fuzzy membership representations in improving the clustering partition. Furthermore, the proposed approach outperformed the non-fuzzy approach SDEC [38] in terms of clustering accuracy for all datasets. In addition, we retested the IDEC model in [37] using the settings reported in the paper: SGD optimizer with a 0.01 learning rate and 0.9 momentum, with the set of pretrained AE weights used for the implementation available online. The results in Table 6 show that the proposed SC-DEC outperformed the IDEC results in terms of clustering accuracy and NMI metrics. Moreover, Figure 2 plots the change trends of the ACC and NMI metrics for the best run on the three datasets. For all datasets, one can observe that the improvement in both metrics stabilized after roughly 30% of the total training time. Then, a slight improvement occurred later that led the model into convergence. Additionally, Figure 3 shows the loss change trend (learning curve) of the run that resulted in the best accuracy for the three datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**MNIST [57]**} & \multicolumn{2}{c}{**STL-10 [59]**} & \multicolumn{2}{c}{**USPS [58]**} \\ \hline
**Metric** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** \\ \hline DL + k-means & 86.83\% & 76.89\% & 86.94\% & 78.84\% & 70.93\% & 68.44\% \\ \hline DEC [21] & 84.30\% & NA & 35.90\% & NA & NA & NA \\ \hline SDEC [38] & 86.11\% & 82.89\% & 38.86\% & 32.84\% & 76.39\% & **77.68\%** \\ \hline IDEC [37] & 83.841\% & 77.885\% & NA & NA & 72.693\% & 71.135\% \\ \hline SC-DEC & **92.11\%** & **84.01\%** & **91.65\%** & **84.85\%** & **76.62\%** & 75.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Clustering results measured in ACC and NMI. Bold formatting indicates the best results. Figure 3: Loss change over training iterations on MNIST (**a**), USPS (**b**), and STL-10 (**c**) datasets.