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  • 标题:K-Means on GPU: A Review
  • 本地全文:下载
  • 作者:Vidya Dhamdhere ; Rahul G. Ghudji
  • 期刊名称:International Journal of Engineering and Computer Science
  • 印刷版ISSN:2319-7242
  • 出版年度:2014
  • 卷号:3
  • 期号:5
  • 页码:5500-5605
  • 出版社:IJECS
  • 摘要:K-Means is the most popular clustering algorithm in data mining. The size of various data sets has increased tremendously day by day. Due to recent development in the shared memory inexpensive architecture like Graphics Processing Units (GPU). The general – purpose applications are implemented on GPU using Compute Unified Device Architecture (CUDA). Cost effectiveness of the GPU and several features of CUDA like thread Divergence and coalescing memory access. Shared memory architecture is much more efficient than distributed memory architecture
  • 关键词:Clustering; k-means; Graphics Processing Unit (GPU); Compute Unified Device Architecture (CUDA); Data Mining
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