Data Clustering Using Social Group Optimization Algorithm

A newly developed optimization technique, known as “Social Group Optimization (SGO),” has been recently introduced by S.C. Satapathy and colleagues. This innovative method presents an effective strategy for addressing global optimization problems. The paper also puts forth a novel approach to cluster data utilizing the SGO method. It demonstrates that SGO can effectively determine the centroids for a user-defined number of clusters, much like other conventional clustering techniques. To assess its performance, the SGO algorithms are evaluated using various datasets and are compared against established clustering methods, including K-means (KMs), Particle Swarm Optimization (PSO) clustering, Differential Evolution (DE) clustering, and Teaching Learning-Based Optimization (TLBO) clustering. The results of these evaluations suggest that the SGO clustering technique holds substantial promise and may outperform other existing techniques.