# Fuzzy Rough C-Mean Based Unsupervised CNN Clustering for Large-Scale Image Data
## 3 Experiments
### Experimental Design  
The experimental results analysis is mainly split into two parts. In the first part, FRUCNN is tested on three benchmark large-scale image datasets with comparison to the other state-of-the-art clustering methods and deep-learning-based clustering methods, and in the second part, we will discuss three different schemes of FRUCNN and experimentally compare their performances in the consequent test. Table 3 shows the results of the performance measure (NMI, Acc, MFM, and MAUC) on four datasets to demonstrate the effectiveness of our proposed approach. From the analysis of the results of all performance measures, MNIST-Full and MNIST-Test show better results compared to ILSVRC12-Val, and YTF. For further analysis of the results of large-scale dataset, we compared our proposed approach to other state-of-the-art methods. In Table 4, the reported results are borrowed from the original paper; for those that did not show the results, the corresponding results are found by re-running the code released by original papers. We put dash marks (-) for the results that are not applied to obtain. Table 4 compares the NMI and ACC performances of the proposed clustering method with other clustering methods with the parameters \(T\) = 0.05 and \(\varepsilon\) = 0.01. We will do the analysis of the results in Table 4 of non-deep-learning based clustering algorithms; these are not applicable for large-scale image dataset like ILRCVRC-12 in ImageNet. In another image dataset such as MNIST and YTF, FCM performed better than K-Mean. When we do the comparison with the results of deep-learning based clustering algorithms with the state-of-the-art clustering algorithms, at that point all deep-learning based clustering algorithms outperform with a significant margin. The reason behind this result that learning performs better is that feature representation of input images lead to better clustering results. We also compare our proposed clustering method with other deep-learning based clustering methods. The proposed FRUCNN achieves comparable performance upgrading in NMI by 0.021-0.032, 0.002-0.013, 0.004-0.005 and 0.043-0.089 with DEPICT, JULE and CCNN methods and significantly outperforms upgrading in NMI by 0.176-0.246. 0.013-0.103, 0.043-0.093 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **ILSVRC12-Val** & \multicolumn{2}{c}{**MNIST-Full**} & \multicolumn{2}{c}{**MNIST-Test**} & \multicolumn{2}{c}{**YTF**} \\ \hline  & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** \\ \hline K-Mean & - & - & 0.500 & 0.534 & 0.501 & 0.547 & 0.776 & 0.601 \\ FCM & - & - & 0.613 & 0.657 & 0.615 & 0.660 & 0.779 & 0.632 \\ \hline DEC & 0.155 & - & 0.816 & 0.844 & 0.827 & 0.859 & 0.446 & 0.371 \\ DEPICT & 0.371 & - & 0.917 & 0.965 & 0.915 & 0.963 & 0.802 & 0.621 \\ CNN-CM & 0.225 & - & 0.906 & - & 0.877 & - & - & - \\ JULE & 0.369 & - & 0.913 & 0.964 & 0.915 & 0.961 & 0.848 & 0.684 \\ CCNN & 0.375 & - & 0.876 & - & 0.916 & - & - & - \\ \hline FR-UCNN & **0.401** & **0.456** & **0.919** & **0.971** & **0.920** & **0.974** & **0.891** & **0.783** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the Different Clustering Algorithms Based on Clustering Performance (NMI & ACC). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **NMI (Normalized** & **ACC (Clustering** & **MFM (Mean** & **MAUC (Mean of Area** \\
**Mutual Information)** & **Accuracy)** & **of F-Measure)** & **Under the Curve)** \\ \hline ILSVRC12 & 0.401 & 0.456 & 0.400 & 0.412 \\ MNIST-Full & 0.919 & 0.971 & 0.870 & 0.924 \\ MNIST-Test & 0.920 & 0.974 & 0.910 & 0.945 \\ YTF & 0.891 & 0.783 & 0.650 & 0.736 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Performance Measures (NMI, Acc, MFM & AUC) of Proposed work (FRUCNN). and 0.445 with DEC and CNN-CM for all ILSVRC 12-Val, MNIST, and YTF datasets. The experiment results demonstrate that the proposed FRUCNN approach performs better for image dataset for numerous scales.