• DocumentCode
    3499823
  • Title

    A GPU based Parallel Hierarchical Fuzzy ART clustering

  • Author

    Kim, Sejun ; Wunsch, Donald C., II

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    2778
  • Lastpage
    2782
  • Abstract
    Hierarchical clustering is an important and powerful but computationally extensive operation. Its complexity motivates the exploration of highly parallel approaches such as Adaptive Resonance Theory (ART). Although ART has been implemented on GPU processors, this paper presents the first hierarchical ART GPU implementation we are aware of. Each ART layer is distributed in the GPU´s multiprocessors and is trained simultaneously. The experimental results show that for deep trees, the GPU´s performance advantage is significant.
  • Keywords
    adaptive resonance theory; computer graphic equipment; coprocessors; fuzzy set theory; multiprocessing systems; parallel architectures; pattern clustering; ART; GPU; adaptive resonance theory; multiprocessors; parallel hierarchical fuzzy clustering; Educational institutions; Graphics processing unit; Kernel; Neural networks; Programming; Subspace constraints; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033584
  • Filename
    6033584