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DOI: 10.14569/IJACSA.2021.0120283
PDF

A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms

Author 1: Boulchahoub Hassan
Author 2: Rachiq Zineb
Author 3: Labriji Amine
Author 4: Labriji Elhoussine

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.

  • Abstract and Keywords
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Abstract: Data Clustering is an interesting field of unsupervised learning that has been extensively used and discussed over several research papers and scientific studies. It handles several issues related to data analysis by grouping similar entities into the same set. Up to now, many algorithms were developed for clustering using several techniques including centroids, density and dendrograms approaches. We count nowadays more than 100 diverse algorithms and many enhancements for each algorithm. Therefore, data scientists still struggle to find the best clustering method to use among this diversity of techniques. In this paper we present a survey on DBSCAN algorithm and its enhancements with respect to time requirement. A significant comparison of DBSCAN versions is also illustrated in this paper to help data scientist make decisions about the best version of DBSCAN to use.

Keywords: Unsupervised learning; clustering; density clustering; DBSCAN

Boulchahoub Hassan, Rachiq Zineb, Labriji Amine and Labriji Elhoussine, “A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120283

@article{Hassan2021,
title = {A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120283},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120283},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Boulchahoub Hassan and Rachiq Zineb and Labriji Amine and Labriji Elhoussine}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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