Automatic Data Clustering based on Hybrid Atom Search Optimization and Sine-Cosine Algorithm

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of interest of scientists and engineers which become a hot topic for different data analysis applications. For example, image clustering, bioinformatics, image segmentation, and natural language processing. Where the process of determining the number and position of centroids is an NP-hard problem. So, this paper presents an alternative automatic clustering algorithm based on the hybrid between the atom search optimization (ASO) and the sine-cosine algorithm (SCA). The main objective of the proposed clustering method, called ASOSCA, is to find automatically the optimal number of centroids and their positions in order to minimize the CS-index (which refers to Compact-separated index). To achieve this goal, the ASOSCA uses SCA as a local search operator to improve the quality of ASO. The performance of the proposed hybrid method is compared with other metaheuristic methods; in which all of them are tested on sixteen clustering datasets and using different cluster validity indexes as Dunn, Silihouette, Davies Bouldin, and Calinski Harabasz. The experimental results show that the ASOSCA depict high superiority in comparison with other types of hybrid metaheuristic in terms of clustering measures.