# Deep Image Clustering Based on Label Similarity and Maximizing Mutual Information across Views
## 4 Experiments
### Results and Analysis  
In Table 3, the quantitative clustering results of the compared methods on four image datasets are orderly reported. From Table 3, our DCSM method consistently achieves superior performance on different datasets, which empirically signifies that DCSM is in a position to cluster data effectively. From further analysis, several tendencies can be observed in Table 3.  
First, the performance of the clustering methods based on deep learning is generally superior to the traditional methods (e.g., K-means, SC). It shows that representation learning is more important than clustering techniques for unsupervised learning.  
Second, the methods (e.g., DEC, DAC) of joint representation learning and cluster assignment outperform traditional methods and the representation-based clustering methods. It indicates that clustering and representation learning can promote each other and achieve better performance consequently.  
Third, the performance of DCSM and IIC using the data augmentation technique is better than other algorithms. It means that the introduction of the data augmentation technique in unsupervised clustering can help the model to be optimized.  
Besides of these common conclusions, our DCSM method outperforms methods that only use class-level representation (e.g., DCSM-oc, DCSM). It means that the clustering performance is related to both class-level representation and instance-level representation.  
Figure 2 visualizes the confusion matrix on the MNIST and Imagenet10 datasets. Figure 3 visualizes the class-level representations of the DCSM on the ImageNet10 datasets using t-SNE at the different training stages.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MNIST**} & \multicolumn{3}{c}{**CIFAR-10**} & \multicolumn{3}{c}{**STL-10**} & \multicolumn{3}{c}{**ImageNet-10**} \\ \cline{2-13}  & **NMI** & **ACC** & **ARI** & **NMI** & **ACC** & **ARI** & **NMI** & **ACC** & **ARI** & **NMI** & **ACC** & **ARI** \\ \hline K-means & 0.501 & 0.572 & 0.365 & 0.087 & 0.229 & 0.049 & 0.125 & 0.192 & 0.061 & 0.119 & 0.241 & 0.057 \\ SC & 0.662 & 0.695 & 0.521 & 0.103 & 0.247 & 0.085 & 0.098 & 0.159 & 0.048 & 0.151 & 0.274 & 0.076 \\ AE & 0.725 & 0.812 & 0.613 & 0.239 & 0.314 & 0.169 & 0.250 & 0.303 & 0.161 & 0.210 & 0.317 & 0.152 \\ DAE & 0.756 & 0.831 & 0.647 & 0.251 & 0.297 & 0.163 & 0.224 & 0.302 & 0.152 & 0.206 & 0.304 & 0.138 \\ GAN & 0.763 & 0.736 & 0.827 & 0.265 & 0.315 & 0.176 & 0.210 & 0.298 & 0.139 & 0.225 & 0.346 & 0.157 \\ DeCNN & 0.757 & 0.817 & 0.669 & 0.240 & 0.282 & 0.174 & 0.227 & 0.299 & 0.162 & 0.186 & 0.313 & 0.142 \\ VAE & 0.876 & 0.945 & 0.849 & 0.245 & 0.291 & 0.167 & 0.200 & 0.282 & 0.146 & 0.193 & 0.334 & 0.168 \\ DEC & 0.771 & 0.843 & 0.741 & 0.257 & 0.301 & 0.161 & 0.276 & 0.359 & 0.186 & 0.282 & 0.381 & 0.203 \\ JULE & 0.913 & 0.964 & 0.927 & 0.192 & 0.272 & 0.138 & 0.182 & 0.277 & 0.164 & 0.175 & 0.300 & 0.138 \\ DAC & 0.935 & 0.977 & 0.948 & 0.396 & 0.522 & 0.306 & 0.366 & 0.470 & 0.257 & 0.394 & 0.527 & 0.302 \\ IIC & - & 0.992 & - & - & 0.617 & - & - & 0.610 & - & - & - & - \\ DCCM & 0.951 & 0.982 & 0.954 & 0.496 & 0.623 & 0.408 & 0.376 & 0.482 & 0.262 & 0.608 & 0.710 & 0.555 \\ ICGAM & - & 0.990 & - & - & **0.700** & - & - & 0.587 & - & - & - & - \\ EDCN & 0.962 & 0.985 & - & **0.603** & 0.544 & - & 0.357 & 0.482 & - & - & - \\ DCSM-oc & 0.950 & 0.981 & 0.959 & 0.451 & 0.589 & 0.368 & 0.431 & 0.576 & 0.342 & 0.561 & 0.701 & 0.523 \\ DCSM & **0.967** & **0.994** & **0.973** & 0.505 & 0.636 & 0.413 & **0.483** & **0.625** & **0.410** & **0.623** & **0.769** & **0.581** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clustering performance of different methods on four datasets. The best results are highlighted in bold.