A rapid review of clustering algorithms

Abstract

Clustering algorithms are essential for organising data into meaningful groups based on inherent patterns and similarities. They play a crucial role across domains such as marketing, e-commerce, healthcare, data analysis, and social media. There is a wide range of clustering algorithms, each with unique strengths and limitations, and no single algorithm is universally optimal for all tasks. In this study, we systematically analyse existing clustering algorithms and classify mainstream approaches across five key dimensions: underlying clustering principles, cluster assignment mechanism, dataset scalability, predefined cluster requirements, and application domains. This classification provides a comprehensive perspective on clustering methodologies, helping researchers select suitable algorithms for specific tasks. In addition, we examine current trends, emerging developments, and future directions in clustering research, highlighting unresolved challenges and open research questions.