# Learning Distributed Representations and Deep Embedded Clustering of Texts
## 5 Experiments
### Algorithm Comparison
#### 5.2.1 Experimental Results on Learning Rates  
To calibrate the learning rates, the values were set from \(10^{-7}\) to \(10^{-3}\) for the Avgnews dataset. The experimental results are shown in Table 1. According to Table 1, With NMI metrics, when the learning rate was \(10^{-5}\), the NMI was the highest, with 0.824, while for SOMR, the highest NMI was 0.743 with learning rate \(10^{-3}\). For Kmeans algorithm, the highest NMI was 0.822 with a learning rate \(10^{-5}\) while for KmeansR, the highest NMI was 0.731 with a learning rate \(10^{-6}\). Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.824, obtained by SOM, followed by 0.822 obtained by Kmeans. For ACC, the highest value of SOM was 0.947 with the learning rate \(10^{-5}\) while it was 0.915 of SOMR with the learning rate \(10^{-3}\). For Kmeans, the highest value of ACC was 0.944 with the same learning rate \(10^{-5}\) as SOM, while for SOMR, the highest ACC was 0.904 with the learning rate \(10^{-6}\). Comparing all algorithm components, the highest value was 0.947, obtained by SOM.  
In regard to calibrating the learning rates for the StackOverflow dataset, the experimental results are shown in Figure 3. According to Figure 3, the SOM and SOMR algorithms were affected by the learning rates, while the Kmeans and KmeansR algorithms had little effect on the learning rates. With NMI metrics, when the learning rate was \(10^{-6}\), the NMI was the highest, with 0.662, while for SOMR, the highest NMI was 0.564 with learning rate \(10^{-5}\). For the Kmeans algorithm, the highest NMI was 0.667 with learning rates \(10^{-6}\), \(10^{-4}\), and \(10^{-3}\), while for KmeansR, the highest NMI was 0.564 with the same corresponding learning rates. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.667, obtained by Kmeans, followed by 0.662, obtained by SOM. For ACC, the highest value of SOM was 0.710 with the learning rate \(10^{-6}\), while it was 0.611 of SOMR, with  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline Dataset & Metrics & Algorithm & \(10^{-7}\) & \(10^{-6}\) & \(10^{-5}\) & \(10^{-4}\) & \(10^{-3}\) \\  & NMI & SOM & 0.763 & 0.739 & **0.824** & 0.565 & 0.625 \\  & NMI & SOMR & 0.724 & 0.729 & 0.696 & 0.491 & 0.743 \\  & NMI & Kmeans & 0.767 & 0.744 & 0.822 & 0.731 & 0.454 \\  & NMI & KmeansR & 0.727 & 0.731 & 0.685 & 0.718 & 0.296 \\ Avgnews & ACC & SOM & 0.922 & 0.909 & **0.947** & 0.804 & 0.836 \\  & ACC & SOMR & 0.899 & 0.903 & 0.887 & 0.767 & 0.914 \\  & ACC & Kmeans & 0.923 & 0.926 & 0.944 & 0.899 & 0.694 \\  & ACC & KmeansR & 0.901 & 0.904 & 0.881 & 0.865 & 0.494 \\ \hline \hline \end{tabular} Bold implies the best values for the NMI and ACC metrics.  
\end{table}
Table 1: Learning rate Calibration and Algorithm Component Comparison (\(10^{-7}\)–\(10^{-3}\)).  
the learning rate \(10^{-6}\). For Kmeans, the highest value of ACC was 0.711 with the same learning rate \(10^{-6}\) as SOM, while for SOMR, the highest ACC was 0.619, with the learning rate \(10^{-6}\), \(10^{-5}\), and \(10^{-3}\). Comparing all algorithm components, the highest value was 0.711, obtained by the Kmeans algorithm.