# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 1 Introduction  
In the ever-expanding landscape of data-driven applications, unsupervised learning techniques play a pivotal role in extracting meaningful patterns from raw data. Clustering, as one of the fundamental tasks in unsupervised learning, seeks to group similar data points together while maintaining separation between distinct clusters [1]. Several research works have been carried out, and various clustering approaches have been proposed, including kernel methods such as support vector machine (SVM) [2], self-organizing maps (SOM) [3], and k-means clustering [4]. However, achieving optimal clustering results remains a challenging endeavor due to various factors such as noisy features, high dimensionality, and the need for robust initialization. Unsupervised feature selection (UFS), on the other hand, aims to identify the most relevant subset of features from the original feature space [5]. By selecting informative features, computational complexity would be reduced, and the quality of clustering results could also be enhanced. Based on evaluation criteria, the current unsupervised feature-selection studies can also be categorized into two primary groups: wrapper and filter-based studies [6]. The evaluation criterion for wrapper-based techniques is the chosen features' classification performance. On the other hand, the assessment criterion in filter techniques remains unaffected by the machine-learning technology. The filter approaches employ a variety of metrics, including distance measurements [7], consistency measures [8], correlation measures [9], and information theory-based measures [10]. Wrapper methods generally outperform filter methods because they evaluate the performance of the unsupervised selected features on a classification algorithm, even though filter methods are usually less computationally expensive [11]. However, these selection techniques continue to face issues with high computational time and convergence to local optima [12]. In addition, traditional unsupervised feature-selection methods often operate independently of the clustering algorithm, overlooking the inherent synergy between feature selection and the clustering process [13]. Metaheuristic techniques have been frequently adopted recently due to their robust global-search capabilities, which help overcome these shortcomings, especially when the number of features increases. Some of these classic metaheuristic algorithms, most widely applied to the unsupervised feature-selection and clustering problems, include the genetic algorithm (GA) [14; 15; 16], particle swarm optimization (PSO) [17; 18; 19], and harmony search algorithms, among others [20; 21]. The SOS technique, first introduced by [22], is a stochastic metaheuristic approach using randomization to determine a collection of solutions. Based on the interactions between species in an ecosystem, the SOS algorithm was designed with a faster convergence time and greater robustness than these classic metaheuristic algorithms [23]. When compared to other population-based metaheuristic algorithms that searched for near-optimal solutions by training a set of candidate solutions using population characteristics to iteratively guide the searching, like the ant colony optimization (ACO) algorithm, the SOS algorithm is better for three key reasons. One benefit of the mutualism and commensal stages of the SOS algorithm is that it concentrates on creating new creatures, which makes it possible for the algorithm to find a variety of solutions. It follows that the algorithm becomes more adept at exploring. For a second reason, the parasitism phase makes the algorithm more exploitation-capable by keeping it from being stuck in local optima. The final benefit is that there are just two general parameters for the SOS algorithm: the maximum number of iterations and the population size. Because of all these benefits, the SOS algorithm is widely used and has been modified to address a variety of optimization issues across a number of industries.