# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 5 Results and Discussion  
### Evaluation of the SOS Cluster Using All Features  
In this section, we evaluate different algorithms, including harmony search as clustering (HSCLUST), k-means, one-step k-means with harmony search (KHS), and SOS, using standard datasets. For all these algorithms, the similarity metric was the cosine correlation measure. Notably, the results presented in this section represent the average performance over 20 runs (to ensure unbiased comparisons). Additionally, each algorithm is executed with 1000 iterations per run. It is worth noting that no specific parameters need to be set up for the k-means algorithm. For SOS, the ecoNeco_size is set to twice the number of classes in the dataset. This paper adapts the same settings as [61][PERSON] et al. (2021) for the other HSCLUST algorithm. Specifically, the authors set the HMS to be twice the number of clusters in the dataset, and PARmax was set to 0.9, PARmin was 0.45, and HMCR was set to 0.6. In Table 6, the algorithmic performances in the document collections are evaluated based on the F-measure. Among the different algorithms, HS + k-means stands out with the highest F-measure. On the other hand, HS as clustering performs poorly. Interestingly, the proposed SOS algorithm is comparable to the k-means algorithm. Specifically, in four datasets, SOS outperforms k-means. In terms of clustering, SOS outperforms HS when compared to the SOS algorithm. As a result, SOS outperforms HS in locating the optimum centroids, while k-means is not very good at locating the global optimal initial centroids. In other words, the SOS is not as powerful in local optima as k-means. Building upon this observation, the next section explores the combination of SOS-based unsupervised feature selection and SOS in clustering. The goal in the subsequent sub-section is to leverage SOS's strength in finding global optima most important features. Evaluation of SOS-Based Unsupervised Feature Selection with SOS Cluster  
As shown in Table 7, the summary comprises the number of features derived from the k-means and bag-of-words (BOW) models, as well as the results of the SOSFS and the SOSC algorithms for cluster performance evaluation. Based on our findings, DS1 had the best F-measure (92.9%) obtained by using the k-means clustering algorithm, whereas DS3 had the lowest F-measure (58.2%). For dataset DS1, the optimization of the PSOC method yielded the highest F-measure of 89.1%. In contrast, for dataset DS3, PSOC resulted in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **k-Means** & **HS** & **SOS** & **KHS** \\ \hline Classic 3 & 0.929 & 0.909 & **0.933** & **0.935** \\ TREC 2001 & 0.804 & 0.829 & **0.831** & **0.853** \\ Newsgroup & 0.582 & 0.611 & **0.649** & **0.659** \\ Routers & 0.636 & 0.682 & **0.702** & **0.712** \\ \hline \hline \end{tabular} Best result: underline and bold; second-best result: bold.  
\end{table} 
Table 6: The result of three cluster algorithms using the external evaluation F-measure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Document** & **Source** & **\#of Document** & **\#of Cluster** \\ \hline DS1 & Classic 3 & 3892 & 3 \\ DS2 & TDT2 and TDT3 of TREC 2001 & 1445 & 53 \\ DS3 & 20 NEWSGROUP & 3831 & 10 \\ DS4 & routers & 4195 & 8 \\ \hline \hline \end{tabular} \# represent the number of documents and clusters in each category.  
\end{table} 
Table 5: Summary description of document set. lowest F-measure of 60.6%. When utilizing the HSC method, dataset DS1 achieved the highest F-measure of 90.9%, whereas dataset DS3 had the lowest F-measure of 61.4%. For the SOSC technique, dataset DS1 produced the highest F-measure of 93.3%, while dataset DS3 recorded the lowest F-measure of 64.9%. Finally, the KHCluster method achieved the maximum F-measure of 93.5% for dataset DS1, but for dataset DS3, KHCluster resulted in the lowest F-measure of 65.9%. For dataset DS1, the integration of SOSFS with k-means achieved the highest F-measure of 93%.