Clustering algorithms: taxonomies, comparative studies, and emerging approaches based on meta-features of datasets

Clustering is a fundamental technique in Data Science used to identify hidden structures and patterns in unlabeled datasets. However, the wide variety of algorithmic families and the heterogeneity of real-world data make the selection of an appropriate clustering algorithm a persistent challenge. This article presents a literature review of clustering algorithms, considering various taxonomies proposed in previous studies and highlighting emerging trends based on dataset meta-features. Our review addresses the main families of clustering algorithms: partitioning, hierarchical, density-based, model-based, and grid-based emphasizing their underlying assumptions, computational properties, and practical advantages. Additionally, comparative studies published over the past two decades are analyzed to identify performance patterns, recurrent limitations, and the scenarios in which each technique tends to perform best. Finally, this article examines contemporary approaches that incorporate meta-features, meta-learning strategies, and algorithm recommendation models aimed at reducing the traditional trial-and-error process through systematic mechanisms that link dataset profiles with suitable algorithms. This state-of-the-art review provides an updated and structured overview of the field, highlighting opportunities to advance toward intelligent clustering algorithm selection.