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
Link To Document