# Learning Distributed Representations and Deep Embedded Clustering of Texts
## 5 Experiments
### Algorithm Comparison
#### 5.2.3 Experimental Results on Temperature  
To calibrate the temperature \(\tau\), the values were set from 0.4 to 0.9. The experimental results are shown in Table 3. According to Table 3, with NMI metrics, when the temperature was 0.9, the NMI was the highest, with 0.966, for SOM and for SOMR, the highest NMI was 0.821. For the Kmeans algorithm, the highest NMI was 0.909, with a temperature of 0.9, and for KmeansR, the highest NMI was 0.907, with the same temperature. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.966, obtained by Kmeans, followed by 0.953, obtained by Kmeans. According to Table 3, with ACC metrics, when the temperature was 0.9, the ACC was the highest, with 0.992, for SOM and for SOMR, the highest NMI was 0.949. For the Kmeans algorithm, the highest ACC was 0.989, with a temperature of 0.9, and for KmeansR, the highest ACC was 0.939, with the same temperature. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest ACC was 0.992, obtained by SOM, followed by 0.989, obtained by Kmeans.  
The experimental results are shown in Figure 5 for the StackOverflow dataset. According to Figure 5, parameter temperature had little effect on SOM, SOMR, Kmeans, and KmeansR algorihtms. With NMI metrics, when the temperature was 0.9, the NMI was the highest, with 0.699. for SOM and for SOMR, the highest NMI was 0.598. For the Kmeans algorithm, the highest NMI was 0.716, with a temperature of 0.7, and for KmeansR, the highest NMI was 0.577 with the same temperature. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest NMI was 0.716, obtained by Kmeans, followed by 0.699, obtained by SOM. According to Figure 5, with ACC metrics, when the temperature was 0.9, the ACC was the highest, with 0.737, for SOM and for SOMR, the highest NMI was 0.639. For Kmeans algorithm, the highest ACC was 0.757, with a temperature of 0.7, and for KmeansR, the highest ACC was 0.632, with the same temperature. Comparing SOM, SOMR, Kmeans, and KmeansR, the highest ACC was 0.757, obtained by Kmeans, followed by 0.737, obtained by SOM.  
Figure 4: Learning Rate Scale Calibration and Algorithm Component Comparison with StackOverflow for NMI and ACC.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline Dataset & Temperature & Metrics & 0.4 & 0.5 & 0.6 & 0.7 & 0.8 & 0.9 \\  & SOM & NMI & 0882 & 0.883 & 0.881 & 0.948 & 0.958 & **0.966** \\  & SOMR & NMI & 0.679 & 0.712 & 0.735 & 0.784 & 0.821 & 0.844 \\  & Kmeans & NMI & 0.881 & 0.904 & 0.905 & 0.905 & 0.909 & 0.953 \\ Avgenews & KmeansR & NMI & 0.765 & 0.744 & 0.764 & 0.784 & 0.807 & 0.819 \\  & SOM & ACC & 0.967 & 0.968 & 0.967 & 0.987 & 0.990 & **0.992** \\  & SOMR & ACC & 0.874 & 0.891 & 0.902 & 0.920 & 0.938 & 0.949 \\  & Kmeans & ACC & 0.966 & 0.974 & 0.975 & 0.975 & 0.976 & 0.989 \\  & Kmeans & ACCR & 0.885 & 0.905 & 0.914 & 0.922 & 0.931 & 0.939 \\ \hline \hline \end{tabular} Bold implies the best values for the NMI and ACC metrics.  
\end{table}
Table 3: Temperature Calibration and Algorithm Component Comparison. (0.4-0.9)According to the above experiments, Kmeans and SOM algorithms were always better than SOMR and KmeansR. For the Avgnews dataset, SOM performed the best, while for the StackOverflow dataset, Kmeans performed the best.