# Learning Distributed Representations and Deep Embedded Clustering of Texts
## 5 Experiments
### Algorithm Comparison
#### 5.2.2 Experimental Results on Learning Rate Scales  
To calibrate the learning rate scale on different clustering algorithms for the Avgenews dataset, the values were set from 50 to 500. The experimental results are shown in Table 2. According to Table 2, with NMI metrics, when the learning rate scale was 500, the NMI for the Avgenews dataset was the highest with 0.883 for SOM, and for SOMR, the highest NMI was 0.712. For the Kmeans algorithm, the highest NMI was 0.904, with a learning rate scale of 500, and for KmeansR, the highest NMI was 0.744, with the same learning rate scale. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.904, obtained by _Kmeans_, followed by 0.883, obtained by SOM. According to Table 2, with ACC metrics, when the learning rate scale was 500, the NMI was the highest, with 0.968, for SOM and for SOMR, the highest NMI was 0.891. For the Kmeans algorithm, the highest ACC was 0.974, with a learning rate scale of 500, and for KmeansR, the highest ACC was 0.905 with the same learning rate scale. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.974, obtained by Kmeans, followed by 0.968, obtained by SOM.  
According to Figure 4, the SOM algorithm was a little better than Kmeans algorithm in NMI and ACC metrics. Similarly, SOMR was slightly better than KmeansR. For the StackOverflow dataset, with NMI metrics, when the learning rate scale was 250, the NMI for the StackOverflow dataset was the highest, with 0.670 for SOM, and for SOMR, the highest NMI was 0.570.  
For the Kmeans algorithm, the highest NMI was 0.680 with a learning rate scale of 250, and for KmeansR, the highest NMI was 0.568, with the same learning rate scale. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.680, obtained by _Kmeans_, followed by 0.670, obtained by SOM. According to Figure 4, with ACC metrics, when the learning rate scale was 250, the NMI was the highest, with 0.716, for SOM and for SOMR, the highest NMI was 0.620, with a learning rate scale of 500. For the Kmeans algorithm, the highest ACC was 0.724 with a learning rate scale of 500 and for KmeansR, the highest ACC was 0.624 with the same learning rate scale. Comparing  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline Dataset & Algorithms & Metrics & 50 & 100 & 150 & 200 & 250 & 500 \\  & SOM & NMI & 0.711 & 0.824 & 0.827 & 0.829 & 0.862 & 0.883 \\  & SOMR & NMI & 0.609 & 0.694 & 0.670 & 0.662 & 0.662 & 0.712 \\ AvgNews & Kmeans & NMI & 0.823 & 0.822 & 0.836 & 0.844 & 0.856 & **0.904** \\  & KmeansR & NMI & 0.699 & 0.685 & 0.664 & 0.663 & 0.669 & 0.744 \\  & SOM & ACC & 0.901 & 0.947 & 0.947 & 0.947 & 0.961 & 0.968 \\  & SOMR & ACC & 0.882 & 0.837 & 0.873 & 0.871 & 0.871 & 0.891 \\  & Kmeans & ACC & 0.944 & 0.944 & 0.950 & 0.953 & 0.958 & **0.974** \\  & KmeansR & ACC & 0.888 & 0.881 & 0.870 & 0.870 & 0.868 & 0.905 \\ \hline \hline \end{tabular} Bold implies the best values for the NMI and ACC metrics.  
\end{table}
Table 2: Learning Rate Scale Calibration and Algorithm Component Comparison (50–500).  
Figure 3: Algorithm Component Comparison with StackOverflow for NMI and ACC.  
KmeansR, the highest NMI was 0.724, obtained by Kmeans, followed by 0.716, obtained by SOM.