A Comparative Analysis of Density Based Clustering Algorithms in Complex Datasets

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dc.contributor.authorCHAIMA, BOUZIDI
dc.contributor.authorNESSRINE KHAWLA, ABDELLAOUI
dc.contributor.authorReporter: BILAL, Lounnas
dc.date.accessioned2025-07-08T07:32:13Z
dc.date.available2025-07-08T07:32:13Z
dc.date.issued2025-06-15
dc.description.abstractThis 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.
dc.identifier.urihttps://repository.univ-msila.dz/handle/123456789/46731
dc.language.isoen
dc.publisherMohamed Boudiaf University of M'sila
dc.subjectDensity-Based Clustering
dc.subjectDBSCAN
dc.subjectNATURAL SCIENCES::Physics::Other physics::Optics
dc.subjectHDBSCAN
dc.subjectComputational Efficiency
dc.subjectNoise Robustness
dc.subjectData Mining
dc.subjectComparative Analysis
dc.subjectSynthetic Datasets
dc.subjectReal-World Data
dc.titleA Comparative Analysis of Density Based Clustering Algorithms in Complex Datasets
dc.typeThesis

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