Sinh-Cosh Optimization-Based Efficient Clustering for Big Data Applications

Data clustering is a pivotal aspect of data mining, attracting significant attention in the exploration of cluster analysis through optimization algorithms. However, challenges arise in the application of optimization-based techniques, stemming from the non-linear nature of the objective function and the complexities inherent in the search space. Clustering, serving as an unsupervised data analysis technique, is employed to discern homogeneous groups of objects by evaluating the values of their attributes. To overcome the above-mentioned drawbacks, we have proposed in this article a new metaheuristic based on a mathematical model called Sinh Cosh Optimizer (SCHO). This optimizer is based on the geometric functions Sinh and Cosh and consists of four key stages: the exploitation and exploration phases, the limited search strategy and the implementation of a switching mechanism. To evaluate the effectiveness of the suggested approach, we conduct a comparative analysis against four widely acknowledged metaheuristic methods found in the literature. The assessment is performed using ten standard datasets sourced from the UCI Machine Learning Repository. These artificial intelligence algorithms are renowned for their promising capabilities in addressing machine learning problems, particularly those related to data clustering. Thorough computational experiments and the analysis of outcomes reveal that the algorithm introduced in this study not only surpasses the performance of the comparison algorithms employed but also demonstrates notable stability. Furthermore, its efficacy extends beyond the experimented scenarios, making it a viable solution for addressing other high-dimensional clustering problems.