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
fDate :
July 31 2011-Aug. 5 2011
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;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033584