VDBSCAN plus : Performance Optimization Based on GPU Parallelism

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-10T22:31:55Z
dc.date.accessioned2022-12-19T20:29:04Z
dc.date.available2020-12-10T22:31:55Z
dc.date.available2022-12-19T20:29:04Z
dc.date.created2020-12-10T22:31:55Z
dc.date.issued2013-01-01
dc.identifier2013 International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 23-28, 2013.
dc.identifierhttp://hdl.handle.net/11449/197449
dc.identifier10.1109/PDCAT.2013.11
dc.identifierWOS:000361018500005
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5378085
dc.description.abstractSpatial 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.
dc.languageeng
dc.publisherIeee
dc.relation2013 International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat)
dc.sourceWeb of Science
dc.subjectspatial data mining
dc.subjectspatial clustering
dc.subjectGPU (Graphics Processing Unit)
dc.subjectVDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise)
dc.titleVDBSCAN plus : Performance Optimization Based on GPU Parallelism
dc.typeActas de congresos


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