Advancements in big data clustering: methods, applications, and insights

Abstract

The digital age has given rise to an unprecedented influx of data, marking the era of big data. In this landscape, clustering has emerged as a critical element of data analysis, enabling the discovery of latent patterns in vast datasets. This review paper explores the state-of-the-art in big data clustering, encompassing influential research, methodologies, advantages, and limitations. The paper highlights the significant advantages brought by different clustering algorithms, spanning domains from smart grids and education to e-commerce and different operations. However, it also acknowledges limitations such as scalability issues and generalization challenges, underlining the importance of addressing these constraints for future research.