# Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
## 4 Experimental Design
### Evaluation Matrices  
We assess the effectiveness of our clustering model by utilizing the most widely utilized evaluation metrics. During the evaluation process, we have established three performance indicators: Clustering Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) [50]. The ACC and NMI metrics are measured on a scale of zero to one, where a higher value reflects exceptional performance and a value of zero represents the lowest possible performance. The ARI metric operates on a scale of \(-1\) to \(1\), with positive values indicating exceptional performance and negative values indicating below-average performance. \[ACC=\underset{m}{max}\ \frac{\sum_{i=i}^{n}1\big{(}c_{i}=m\big  
Our model FIDC has achieved an accuracy of 84% on the Amazon-Fashion dataset, outperforming other models in all evaluation metrics. As a baseline test, the same network was applied with optimal parameters on the MNIST handwritten and Fashion-MNIST datasets, resulting in 94% accuracy and 68% accuracy, respectively. Figure 8 compares FDIC's performance on the Amazon-Fashion dataset with other commonly used techniques, such as DEC, IDEC, DCN, VaDE, and JULE. The experimental results are also presented visually in Figure 9, where each visual space represents a single cluster. Our model, FIDC, shows improved results compared to other methods studied. The efficiency of FIDC is commendable, and its latent space representation is relatively small compared to the input. The improved feature learning and compact latent space representation and distribution contribute to the superior performance of FIDC. The convergence of the model is rapid and consistent. After reaching 100 epochs, FIDC demonstrates state-of-the-art efficiency. We use the confusion matrix, also known as an error matrix, to visually represent the clustering results. Figure 10, displays this matrix, where the rows represent the actual labels and the columns represent the labels assigned by clustering. As clustering is an unsupervised learning technique, it does not have pre-existing labels or target outputs for the data points. The largest numbers in the matrix are not found on the diagonal line. In this specific case, we chose to match the colors even though the clusters did not line up exactly, as we were aware of the actual situation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Amazon-Fashion**} & \multicolumn{2}{c}{**MNIST**} & \multicolumn{3}{c}{**Fashion-MNIST**} \\ \cline{2-10}  & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** \\ \hline K-means & 0.587 & 0.705 & 0.481 & 0.532 & 0.499 & 0.365 & 0.474 & 0.499 & 0.512 \\ AE + k-means & 0.749 & 0.765 & 0.617 & 0.815 & 0.785 & 0.720 & 0.482 & 0.556 & 0.390 \\ VAE + k-means & 0.743 & 0.685 & 0.615 & 0.793 & 0.716 & - & 0.485 & 0.513 & - \\ AAE + k-means & 0.776 & 0.672 & 0.632 & 0.813 & 0.743 & - & 0.512 & 0.534 & - \\ DEC [16] & 0.780 & 0.678 & 0.590 & 0.865 & 0.827 & 0.740 & 0.516 & 0.546 & 0.419 \\ IDEC [17] & 0.791 & 0.716 & 0.633 & 0.884 & 0.867 & 0.881 & 0.529 & 0.557 & 0.428 \\ DCN [18] & 0.723 & 0.699 & 0.614 & 0.831 & 0.825 & 0.756 & 0.531 & 0.571 & 0.386 \\ VaDE [34] & 0.801 & 0.745 & 0.662 & 0.945 & 0.876 & - & 0.578 & 0.630 & - \\ JULE [33] & **0.809** & **0.752** & **0.679** & **0.964** & **0.913** & **-** & **0.563** & **0.608** & **-** \\ _FIDC (Proposed)_ & **0.843** & **0.756** & **0.694** & **0.940** & **0.887** & **0.893** & **0.681** & **0.692** & **0.561** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering performance of various methods on open-source datasets expressed in terms of accuracy (ACC), normalized mutual information (NMI), and adjusted Rand index (ARI). All baseline method results were obtained from the corresponding code or original papers. The result is not available, indicated by the mark “-”. Figure 8: Performance comparison of Amazon-Fashion clustering.