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Description
  • Unsupervised clustering of large data sets is a complicated task. Due to its complexity, various meta-heuristic machine learning algorithms have been used to automate the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. The GPU computing is a recent programming paradigm introducing high performance parallel computing to general audience. This work presents an acceleration of a genetic algorithm for density based clustering on the GPU using the nVidia compute unified device architecture (CUDA)
  • Unsupervised clustering of large data sets is a complicated task. Due to its complexity, various meta-heuristic machine learning algorithms have been used to automate the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. The GPU computing is a recent programming paradigm introducing high performance parallel computing to general audience. This work presents an acceleration of a genetic algorithm for density based clustering on the GPU using the nVidia compute unified device architecture (CUDA) (en)
Title
  • Evolutionary clustering on CUDA
  • Evolutionary clustering on CUDA (en)
skos:prefLabel
  • Evolutionary clustering on CUDA
  • Evolutionary clustering on CUDA (en)
skos:notation
  • RIV/61989100:27240/12:86088876!RIV14-MSM-27240___
http://linked.open...avai/predkladatel
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  • P(ED1.1.00/02.0070), S
http://linked.open...vai/riv/dodaniDat
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  • 135318
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  • RIV/61989100:27240/12:86088876
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  • gpu; clustering (en)
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  • [C2D94AA4B6F9]
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  • Praha
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  • Amsterodam
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  • Frontiers in Artificial Intelligence and Applications. Volume 251
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  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
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http://linked.open.../riv/zahajeniAkce
issn
  • 0922-6389
number of pages
http://bibframe.org/vocab/doi
  • 10.3233/978-1-61499-098-7-909
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  • IOS Press
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  • 978-1-61499-176-2
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  • 27240
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