# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 5 Results and Discussion
### Evaluation of the SOS Cluster Using All Features
The proposed symbiotic organisms search-based optimal unsupervised feature selection (SOSUFS) in conjunction with symbiotic organism search-based optimal clustering (SOSC) was found to be the best text clustering strategy. Following the evaluation, the SOSFS algorithm combined with the k-means algorithm demonstrated a relatively strong performance. The SOSC and particle swarm optimization clustering (PSOC) algorithms also showed moderate performance. In contrast, the KHCluster method ranked third in terms of performance. Out of all the approaches that were assessed, the k-means algorithm performed the worst. Text clustering performance has been demonstrated to be enhanced by using the UFS with the k-means algorithm. A statistical evaluation of the proposed hybridized SOS multi-objective methods for clustering, unsupervised feature selection was conducted. The optimal performance of text clustering can be assessed, as can whether significant differences can be obtained, using this multi-objective method for clustering and unsupervised feature selection. Based on the Friedman criterion, Table 10 shows the ranks of the multi-objectives proposed for the clustering, unsupervised feature selection and other methods. The criteria establish the rating, with a lower value signifying a higher rank. Table 10 demonstrates that our unsupervised feature selection and grouping technique, which employs SOS, had the lowest value and was ranked highest. The last two rows of Table 10 display the Friedman and Iman-Davenport \(p\)-values. Hybridize SOS in unsupervised feature selection with SOSC, KHCluster, SOSCe, WDOC, HSC, PSOC, and k-means is in the second, third, fourth, fifth, sixth, and seventh ranks, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Ranking** \\ \hline k-means & 10.18 \\ PSOC & 10.15 \\ HSC & 10.01 \\ WDOC & 9.61 \\ SOSC & 9.39 \\ KHCluster & 9.3 \\
**SOSC and SOSFS** & **8.53** \\ Friedman test (\(p\)-value) & 0.00 \\ man-Davenport (\(p\)-value) & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The ranking of the proposed algorithms using the Friedman test. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Comparison** & **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 SOSFS with SOSC & **7096** & **0.953** & **4731** & **0.865** & **10,346** & **0.686** & **5651** & **0.734** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The f-measurement of the proposed hybrid multi-objective clustering.