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The document reviews the DBSCAN algorithm, a pioneering density-based clustering technique, and its various enhancements aimed at addressing its limitations, such as the need for user-defined parameters and computational complexity. It categorizes clustering methods and evaluates different DBSCAN variations like VDBSCAN, FDBSCAN, and IDBSCAN, highlighting their strengths and weaknesses. The paper concludes with a critical assessment of these variations and suggests future research directions in clustering a

Author

13908353380

Language

EN