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Volumn 8752, Issue , 2013, Pages

Performance impact of dynamic parallelism on different clustering algorithms

Author keywords

CUDA 5.0; Divisive hierarchical clustering; K means

Indexed keywords

CUDA 5.0; DATA DEPENDENCIES; DATA-DEPENDENT ALGORITHMS; DIVISIVE HIERARCHICAL CLUSTERING; HIER-ARCHICAL CLUSTERING; K-MEANS; K-MEANS CLUSTERING; PERFORMANCE IMPACT;

EID: 84881116805     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2018069     Document Type: Conference Paper
Times cited : (25)

References (5)
  • 1
    • 84879820077 scopus 로고    scopus 로고
    • URL, August
    • NVIDIA, Cuda Dynamic Parallelism Programming Guide. URL: http://docs.nvidia.com/cuda/pdf/CUDA-Dynamic-Parallelism-Programming-Guide.pdf (August 2012).
    • (2012) Cuda Dynamic Parallelism Programming Guide
  • 2
    • 2442439674 scopus 로고    scopus 로고
    • A comparison of document clustering techniques
    • Steinbach, M., Karypis, G., and Kumar, V., "A comparison of document clustering techniques, " KDD Workshop on Text Mining, 400(1), 525-526 (2000).
    • (2000) KDD Workshop on Text Mining , vol.400 , Issue.1 , pp. 525-526
    • Steinbach, M.1    Karypis, G.2    Kumar, V.3
  • 3
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond K-means
    • Jain, A. K., "Data clustering: 50 years beyond K-means., " Pattern Recognition Letters, 31(8), 651-666 (2010).
    • (2010) Pattern Recognition Letters , vol.31 , Issue.8 , pp. 651-666
    • Jain, A.K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.