VDBSCAN plus : Performance Optimization Based on GPU Parallelism
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Data
2013-01-01
Orientador
Coorientador
Pós-graduação
Curso de graduação
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Editor
Ieee
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Trabalho apresentado em evento
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Resumo
Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this area. This work proposes a new algorithm referred to as VDBSCAN+, which derived from the algorithm VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise) and focuses on the use of parallelism techniques in GPU (Graphics Processing Unit), obtaining a significant performance improvement, by increasing the runtime by 95% in comparison with VDBSCAN.
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Inglês
Como citar
2013 International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 23-28, 2013.
