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In the contemporary era of data-driven processes, addressing the challenge of processing vast volumes of data has become a pressing concern. With the rapid advancement of computer science and information technology, data processing efficiency has significantly improved. Within this expansive domain, three prominent clustering techniquesnamely, K-Means clustering, spectral clustering, and Density-based spatial clustering of applications with noise (DBSCAN)have assumed pivotal roles due to their versatility and effectiveness. This essay embarks on a systematic examination of these three methods, deconstructing their fundamental principles and navigating through their practical applications.
Chen et al. (Thu,) studied this question.