Data Clustering Using Tangent Search Algorithm

This article proposes a new algorithm, called Clustering Tangent Search Algorithm (C-TSA) to solve clustering problems. The C-TSA uses Tangent Search Algorithm (TSA) as an optimizer to move a given solution toward a better solution. The main characteristics of this new optimization algorithm are its simplicity, as it requires only a small number of user-defined parameters, and its efficiency in data clustering as demonstrated in the experimental results, where it provides very promising and competitive results on many benchmark datasets. TSA optimizer has been designed with an effective balance between exploration and exploitation. Additionally, it includes a useful local escape mechanism to prevent getting stuck in local optima, and an adaptive step size to improve convergence towards the best solution.