# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 2 Review of Related Works
### Clustering Algorithms Based on SOS Methods  
According to [26; 49; 53], SOS algorithms have been widely used for classification problems but not yet evaluated for the unsupervised feature-selection and clustering problems. An unsupervised technique for analyzing data referred to as clustering is used to find sets of objects that are homogeneous based on the values of their properties. Clustering can be divided into two primary categories: partitional clustering methods and hierarchical clustering methods. The recurrent hierarchical grouping of data objects is an essential component of the hierarchical clustering technique. In addition, a single partition of a dataset is created using the partitional clustering technique to find the underlying categories in the data. This method is non-hierarchical and is based on the application of a predetermined objective function. k-means is an example of this. In an effort to further solve problems associated with automatic data clustering, an automatic k-means clustering-based SOS (CSOS) was proposed by [54]. By integrating the global and local search strategies in a sub-ecosystem built on the automated k-means clustering technique, the CSOS algorithm essentially performs a hybrid search approach. Mutualism and commensalism are the two distinct phases that are covered by the local \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**SOS Variations**} & **Feature-Selection** & \multirow{2}{*}{**References**} & **Supervised or** \\  & & **Approaches** & & **Unsupervised Learning** \\ \hline
1 & Modified SOS & Wrapped based & [20] & Unsupervised learning \\ \hline
2 & Hybrid method & Wrapper/coating-based & [21] & Unsupervised learning \\ \hline
3 & Multi-objective SOS & Wrapper based & [26] & Unsupervised learning \\ \hline
4 & Modified SOS & Wrapper based & [31] & Unsupervised learning \\ \hline
5 & Improved SOS & Wrapper based & [46] & Supervised learning \\ \hline
6 & Modified SOS & Filter based & [47] & Unsupervised learning \\ \hline
7 & MOEA & Wrapper based & [48] & Unsupervised learning \\ \hline \multirow{4}{*}{8} & Five distinct SOS algorithms & \multirow{2}{*}{Wrapper based} & \multirow{2}{*}{[49]} & \multirow{2}{*}{Supervised learning} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline
9 & Hybrid approach & Filter and wrapper-based & [50] & Supervised learning \\ \hline
10 & Hybrid method & - & [51] & Unsupervised learning \\ \hline
11 & Hybrid method & & [52] & Supervised learning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A list of the various feature-selection techniques and SOS algorithms. search. Furthermore, in CSOS global search, this is referred to as the parasitism phase, in which only the best solution within a cluster can communicate with the best solutions in other clusters. Additionally, a data categorization methodology combining an SOS algorithm and a regularized extreme learning machine was developed by [55]. The newly developed algorithm, SOS-RELM, consists of two separate SVM phases: backpropagation and least-squares support vector machines. As a result, the SOS method is both an effective and efficient optimization approach. In the second phase, it optimizes the regularization parameters, invisible biases, and input weights. Moreover, [49] presented DSOS algorithms in a different study for the optimization of neighborhood size and feature subsets as supervised learning. Furthermore, [53] presented a method for automatic data clustering that makes use of the SOS algorithm. In this article, we have discussed the ease of trapping in a local optimum and the strong dependence of the k-means clustering algorithm on the beginning solution. To create clusters, the SOS framework used an automated k-means clustering algorithm. The most effective solutions interact with one another within each cluster, combining local and global search techniques [56]. However, such an approach leads to a rise in computational cost.