# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 5 Results and Discussion
### Evaluation of the SOS Cluster Using All Features
Conversely, for dataset DS3, the same integration produced the lowest F-measure of 60.2%. The SOSFS algorithm effectively reduced the number of features in DS1 by 8186 out of 13,310, in DS2 by 5573 out of 6737, in DS3 by 20,854 out of 27,211, and in DS4 by 9561 out of 1215.When SOSFS was combined with PSO, the lowest F-measure of 63.6% was observed for dataset DS3. The feature reduction for SOSFS with PSO was significant: 9927 out of 13,310 features in DS1, 4826 out of 6737 in DS2, 13,824 out of 27,211 in DS3, and 5084 out of 12,152 in DS4.With SOSFS and HSC, dataset DS3 had the lowest F-measure of 62.1%. Feature reduction was also substantial: 10,843 out of 13,310 in DS1, 5128 out of 6737 in DS2, 11,843 out of 27,211 in DS3, and 108,54 out of 12,152 in DS4. When the SOSFS algorithm was combined with WDOC, dataset DS3 yielded the lowest F-measure of 65%. In DS1, DS2, DS3, and DS4, SOSFS effectively decreased the number of features by 9824 out of 13,310, 5834 out of 6737, 19,283 out of 27,211, and 6891 out of 12,152. The lowest F-measure of 66.8% was achieved for dataset DS3 when the SOSFS algorithm was combined with the KHCluster. In DS1, DS2, DS3, and DS4, SOSFS effectively decreased the number of features by 10,289 out of 13,310, 6057 out of 6737, 20,851 out of 27,211, and 5732 out of 12,152. For SOSUF reduction in DS1, k-means was the best. In DS2, SOSUFS was best in PSOC; in DS3, it was best with HSC; and in DS4, it was best with PSOC. Comparing Tables 7 and 8, the KHCluster showed the best performance and was more powerful than used all the features. From Tables 7 and 8, we can observe that the SOSUFS proved that some of the features mis-cluster and reduce the performance of cluster \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Comparison** & \multicolumn{2}{c}{**DS1 Classic 3**} & \multicolumn{2}{c}{**DS2 TREC 2001**} & \multicolumn{2}{c}{**DS3 Newsgroup**} & \multicolumn{2}{c}{**DS4 Routers**} \\ \hline  & **Features** & **F-Measure** & **Features** & **F-Measure** & **Features** & **F-Measure** & **Features** & **F-Measure** \\ \hline k-means & 13,310 & 0.929 & 6737 & 0.804 & 27,211 & 0.582 & 12,152 & 0.636 \\ \hline PSOC & 13,310 & 0.891 & 6737 & 0.841 & 27,211 & 0.606 & 12,152 & 0.688 \\ \hline HSC & 13,310 & 0.909 & 6737 & 0.829 & 27,211 & 0.611 & 12,152 & 0.682 \\ \hline WDOC & 13,310 & **0.933** & 6737 & **0.83** & 27,211 & **0.64** & 12,152 & **0.69** \\ \hline KHCluster & 13,310 & **0.935** & 6737 & **0.853** & 27,211 & **0.659** & 12,152 & **0.712** \\ \hline \hline \end{tabular} Best result: underline and bold; second-best result: bold.  
\end{table} 
Table 7: The f-measurement of the cluster using all features. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Comparison** & \multicolumn{2}{c}{**DS1 Classic 3**} & \multicolumn{2}{c}{**DS2 TREC 2001**} & \multicolumn{2}{c}{**DS3 Newsgroup**} & \multicolumn{2}{c}{**DS4 Routers**} \\ \hline  & **Features** & **F-Measure** & **Features** & **F-Measure** & **Features** & **F-Measure** & **Features** & **F-Measure** \\ \hline k-means & **8186** & 0.93 & 5573 & 0.824 & 20,854 & 0.602 & 9561 & 0.636 \\ \hline PSOC & 9927 & 0.928 & **4826** & 0.847 & **13,824** & 0.636 & **5084** & 0.688 \\ \hline HSC & 10,843 & 0.929 & **5128** & 0.831 & **11,843** & 0.621 & 10,854 & 0.682 \\ \hline WDOC & **9824** & **0.939** & 5834 & **0.83** & 19,283 & **0.64** & 6891 & **0.69** \\ \hline KHCluster & 10,289 & **0.949** & 6057 & **0.861** & 20,851 & **0.668** & **5732** & **0.748** \\ \hline \hline \end{tabular} Best result: underline and bold; second-best result: bold.  
\end{table} 
Table 8: The f-measurement of the proposed SOS-based unsupervised feature selection. algorithms. In all cluster algorithms, the SOSUFS enhances the performance when looking to the F-measure. As seen in Table 9, when combined with SOSC, the SOSFS approach produced the highest F-measure for dataset DS1: 95.3%. In DS1, DS2 and DS3, 10,346 and 12,152 features, 7096 and 4731 features, and 5651 and 12,152 features, respectively, were successfully reduced by SOSFS.