A Comparative Analysis of Density Based Clustering Algorithms in Complex Datasets
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Date
2025-06-15
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
This dissertation conducts a comparative analysis of density-based clustering algorithms, fo cusing on their performance in complex datasets. The study examines popular algorithms
such as DBSCAN, OPTICS, and HDBSCAN, evaluating them across diverse criteria, in cluding cluster quality, computational efficiency, and robustness to noise. Through exper iments on real-world and synthetic datasets, the research aims to identify the strengths and
limitations of each algorithm, providing valuable insights for selecting appropriate methods
in various data mining applications.
Description
Keywords
Density-Based Clustering, DBSCAN, NATURAL SCIENCES::Physics::Other physics::Optics, HDBSCAN, Computational Efficiency, Noise Robustness, Data Mining, Comparative Analysis, Synthetic Datasets, Real-World Data