# Improved Selective Deep-Learning-Based Clustering Ensemble
## 4 Experiments
### ISDCE t-SNE Visualization  
In this section, to verify that ISDCE could achieve good clustering results, we adopted t-SNE to visualize the initial embedding and final embedding clustering results on Iris and Mnist5 datasets. The visualization results are shown in Figures 3-6. The horizontal and vertical coordinates of tne_1, tne_2 in the figure are the scalar numerical coordinates of the initial embedding and the final embedding reduced to 2D features after t-SNE. The number of clusters visualized is the true number of categories on the dataset. Points of different colors represent samples in different clusters. It can be seen that the final embedding after joint optimization with clustering loss is more cluster-friendly and produces higher-quality clusters. We can easily observe that the final embedding trained by the joint clustering loss forms denser clusters with fewer mixed clusters. For the visualization results of the Iris data, the data are characterized in that only one class in the Iris data is linearly separable, and the other two classes are linearly indivisible. Therefore, visualizing the Iris results with t-SNE results in a situation where there are two clusters that are indistinguishable. But, in fact, from the two clustering evaluation measures, ISDCE indeed achieved good performance on these data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Cars** & **Iris** & **Wine** & **Mnist5** & **MNIST** & **Strain** & **Specices** \\ \hline DAE & 0.228 (0.028) & 0.741 (0.055) & 0.416 (0.046) & 0.555 (0.034) & 0.750 (0.025) & 0.943 (0.007) & 0.987 (0.003) \\ \hline ISDCE\_noSS & 0.246 (0.016) & 0.762 (0.012) & 0.448 (0.023) & 0.573 (0.018) & 0.758 (0.013) & 0.955 (0.005) & 0.989 (0.002) \\ \hline ISDCEQS & 0.258 (0.002) & 0.785 (0.005) & 0.542 (0.003) & 0.585 (0.010) & 0.801 (0.002) & 0.959 (0.001) & 0.993 (0.002) \\ \hline ISDCEDS & 0.278 (0.010) & 0.792 (0.010) & 0.465 (0.014) & 0.584 (0.012) & 0.776 (0.003) & 0.958 (0.002) & 0.993 (0.001) \\ \hline ISDCEBS & 0.256 (0.005) & 0.788 (0.001) & 0.523 (0.004) & 0.590 (0.003) & 0.800 (0.001) & 0.958 (0.001) & 0.993 (0.002) \\ \hline \end{tabular}
\end{table}
Table 6: Average **NMI** results of the ISDCE component ablation experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Cars** & **Iris** & **Wine** & **Mnist5** & **MNIST** & **Strain** & **Specices** \\ \hline DAE & 0.062 (0.054) & 0.731 (0.056) & 0.397 (0.076) & 0.430 (0.042) & 0.739 (0.047) & 0.928 (0.030) & 0.948 (0.032) \\ \hline ISDCE\_noSS & 0.075 (0.036) & 0.756 (0.013) & 0.434 (0.028) & 0.461 (0.017) & 0.745 (0.014) & 0.938 (0.010) & 0.961 (0.020) \\ \hline ISDCEQS & 0.062 (0.001) & 0.774 (0.007) & 0.525 (0.005) & 0.475 (0.008) & 0.811 (0.005) & 0.943 (0.002) & 0.972 (0.003) \\ \hline ISDCEDS & 0.218 (0.028) & 0.782 (0.015) & 0.449 (0.015) & 0.470 (0.015) & 0.754 (0.008) & 0.944 (0.003) & 0.971 (0.005) \\ \hline ISDCEBS & 0.063 (0.001) & 0.775 (0.001) & 0.494 (0.007) & 0.481 (0.001) & 0.808 (0.003) & 0.944 (0.001) & 0.972(0.002) \\ \hline \end{tabular}
\end{table}
Table 7: Average **ARI** results of the ISDCE component ablation experiment.  
Figure 4: Final embedding t-SNE visualization of Iris dataset.  
Figure 5: Initial embedding t-SNE visualization of Mnist5 dataset.  
Figure 3: Initial embedding t-SNE visualization of Iris dataset.  
Figure 6: Final embedding t-SNE visualization of Mnist5 dataset.