# Improved Selective Deep-Learning-Based Clustering Ensemble
## 4 Experiments
### Experimental Settings and Clustering Performance Comparison
From the ARI results in Table 5, it can be observed that three strategies of ISDCE also obtained the best scores on all image and biological datasets. Due to the large number of comparison methods and experimental data involved, this paper does not show the execution time of each algorithm. However, from the experimental results of three image datasets with gradually increasing sample sizes, it is observed that the SECWK algorithm is the fastest, followed by LWGP and our ISDCE. The LRTA method has the slowest running efficiency. In addition, WSCE, LRTA, ECCMS, and SPACE grow exponentially in running efficiency with an increase in data samples, thus failing to obtain the final clustering results on MNIST data for a longer period of time. The execution time of the remaining methods is in the same order of magnitude. In the following, we will conduct a series of experiments to verify the validity of each component of our ISDCE approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mnist5** & **MNIST** & **Strain** & **Speices** \\ \hline WSCE & 0.543 (0.008) & **timeout** & **error** & **error** \\ \hline PTAAL & 0.495 (0.012) & 0.497 (0.013) & 0.815 (0.045) & 0.899 (0.007) \\ \hline PTACL & 0.501 (0.011) & 0.491 (0.016) & 0.813 (0.040) & 0.894 (0.008) \\ \hline PTASL & 0.354 (0.080) & 0.038 (0.034) & 0.788 (0.050) & 0.914 (0.011) \\ \hline PTGP & 0.500 (0.011) & 0.494 (0.017) & 0.833 (0.038) & 0.860 (0.019) \\ \hline SECWK & 0.470 (0.028) & 0.470 (0.026) & 0.762 (0.056) & 0.836 (0.016) \\ \hline LWEA & 0.513 (0.006) & 0.506 (0.007) & 0.945 (0.007) & 0.981 (0.005) \\ \hline LWGP & 0.495 (0.005) & 0.498 (0.004) & 0.947 (0.008) & 0.986 (0.003) \\ \hline LRTA & 0.528 (0.007) & **timeout** & 0.950 (0.007) & **timeout** \\ \hline ECCMS & 0.345 (0.114) & **timeout** & 0.946 (0.006) & **timeout** \\ \hline SPACE & 0.444 (0.006) & **timeout** & 0.861 (0.011) & **timeout** \\ \hline ISDCEQS & **0.585 (0.010)** & **0.801 (0.002)** & **0.959 (0.001)** & **0.993 (0.002)** \\ \hline ISDCEDS & **0.584 (0.012)** & **0.776 (0.003)** & **0.958 (0.002)** & **0.993 (0.001)** \\ \hline ISDCEBS & **0.590 (0.003)** & **0.800 (0.001)** & **0.958 (0.001)** & **0.993 (0.002)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average **NMI** results of the comparison method with ISDCE on two **image** and two **biological** datasets (The best three scores in each column are highlighted in bold). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mnist5** & **MNIST** & **Strain** & **Speices** \\ \hline WSCE & 0.377 (0.016) & **timeout** & **error** & **error** \\ \hline PTAAL & 0.372 (0.020) & 0.382 (0.024) & 0.567 (0.142) & 0.463 (0.037) \\ \hline PTACL & 0.380 (0.019) & 0.372 (0.022) & 0.578 (0.126) & 0.449 (0.046) \\ \hline PTASL & 0.174 (0.084) & 0.003 (0.004) & 0.476 (0.137) & 0.582 (0.068) \\ \hline PTGP & 0.382 (0.018) & 0.377 (0.032) & 0.673 (0.106) & 0.353 (0.067) \\ \hline SECWK & 0.317 (0.048) & 0.346 (0.036) & 0.410 (0.138) & 0.175 (0.034) \\ \hline LWEA & 0.393 (0.009) & 0.397 (0.014) & 0.932 (0.024) & 0.846 (0.021) \\ \hline LWGP & 0.370 (0.006) & 0.384 (0.008) & 0.934 (0.024) & 0.869 (0.018) \\ \hline LRTA & 0.404 (0.013) & **timeout** & 0.932 (0.024) & **timeout** \\ \hline ECCMS & 0.170 (0.073) & **timeout** & 0.894 (0.036) & **timeout** \\ \hline SPACE & 0.319 (0.003) & **timeout** & 0.724 (0.037) & **timeout** \\ \hline ISDCEQS & **0.475 (0.008)** & **0.811 (0.005)** & **0.943 (0.002)** & **0.972 (0.003)** \\ \hline ISDCEDS & **0.470 (0.015)** & 0.754 (0.008) & **0.944 (0.003)** & **0.971 (0.005)** \\ \hline ISDCEBS & **0.481 (0.001)** & **0.808 (0.003)** & **0.944 (0.001)** & **0.972 (0.002)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average **ARI** results of the comparison method with ISDCE on two **image** and two **biological** datasets (The best three scores in each column are highlighted in bold).