# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 6 Conclusions  
This study investigates a multi-objective symbiotic organism search algorithm for unsupervised feature selection that simultaneously takes into account the number of selected features and the accuracy of the clustering. The effectiveness of the proposed approach is examined using reputable benchmark datasets. Based on the results, the proposed feature-selection model offered a feature subset of high quality for feature selection, and the final model outperformed the other techniques in terms of clustering accuracy for both datasets. The fact that the proposed feature-selection technique produced satisfactory results for both datasets indicates that it may be able to provide a potential solution to this problem. In addition, the robustness of the model across datasets is also demonstrated by this finding. However, in comparison to the traditional unsupervised feature-selection algorithms, the computing cost of the proposed algorithm is significant. Although unsupervised feature selection is typically an offline procedure, this is not a total disadvantage. Additionally, hybrid techniques are presented in this paper and applied to all datasets for unsupervised feature selection. These algorithms were shown to have both strengths and limitations. Out of all the algorithms discussed in this study, the combination of SOSFS and SOSC produced the best clustering accuracy results while reducing features that are not relevant. This approach presents an optimal solution set instead of a single solution and demonstrates the significance of handling feature-selection problems as a multi-objective process. As a result, this study concludes that the proposed SOSFS is a realistic and efficient method for solving unsupervised feature-selection problems. Additionally, the proposed SOSC model outperformed the other algorithms for both datasets in terms of clustering accuracy. Furthermore, various datasets for feature selection and clustering may also be used to testthe proposed hybrid approach. In addition, future research needs to examine the nature of features chosen by the SOSFS algorithm when combine with k-means and SOSC, which have not yet been evaluated.  
Conceptualization, A.F.J.A.-G., M.Z.A.N. and Z.A.A.A.; methodology, A.F.J.A.-G., M.Z.A.N. and Z.A.A.A.; software, A.F.J.A.-G.; validation, A.F.J.A.-G., M.Z.A.N. and M.R.B.Y.; formal analysis, A.F.J.A.-G., M.Z.A.N. and M.R.B.Y.; investigation, A.F.J.A.-G. and M.Z.A.N.; resources, A.F.J.A.-G., M.Z.A.N. and Z.A.A.A.; data curation, A.F.J.A.-G.; writing--original draft preparation, A.F.J.A.-G., M.Z.A.N. and M.R.B.Y.; writing--review and editing, A.F.J.A.-G., M.Z.A.N. and Z.A.A.A.; visualization, A.F.J.A.-G.; supervision, M.Z.A.N. and M.R.B.Y.; project administration, Z.A.A.A.; funding acquisition, A.F.J.A.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Malaysia Ministry of Higher Education and the Universiti Kebangsaan Malaysia under the Q2678Q25 FRGS/1/2022/ICT02/UKM/02/3 with machines and materials.  
The dataset used in this work based on [64; 65].  
We thank the anonymous reviewers for their comments and suggestions.  
The authors declare no conflicts of interest.