| Authors | Hamid Saadatfar |
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| Journal | Journal of Supercomputing |
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| Page number | 6214-6235 |
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| Serial number | 77 |
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| Volume number | 6 |
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| IF | 1.326 |
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| Paper Type | Full Paper |
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| Published At | 2021 |
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| Journal Grade | ISI |
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| Journal Type | Electronic |
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| Journal Country | Iran, Islamic Republic Of |
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| Journal Index | JCR،Scopus |
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Abstract
Abstract
Big data storage and processing are among the most important challenges now.
Among data mining algorithms, DBSCAN is a common clustering method. One
of the most important drawbacks of this algorithm is its low execution speed. This
study aims to accelerate the DBSCAN execution speed so that the algorithm can
respond to big datasets in an acceptable period of time. To overcome the problem, an
initial grouping was applied to the data in this article through the K-means++ algorithm.
DBSCAN was then employed to perform clustering in each group separately.
As a result, the computational burden of DBSCAN execution reduced and the clustering
execution speed increased significantly. Finally, border clusters were merged
if necessary. According to the results of executing the proposed algorithm, it managed
to greatly reduce the DBSCAN execution time (98% in the best-case scenario)
with no significant changes in the qualitative evaluation criteria for clustering.
Paper URL
