A GPU Based Parallel Hierarchical Fuzzy ART Clustering
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.
Recommended Citation
S. Kim and D. C. Wunsch, "A GPU Based Parallel Hierarchical Fuzzy ART Clustering," Proceedings of the International Joint Conference on Neural Networks, pp. 2778 - 2782, Institute of Electrical and Electronics Engineers (IEEE), Jan 2011.
The definitive version is available at https://doi.org/10.1109/IJCNN.2011.6033584
Meeting Name
2011 International Joint Conference on Neural Network, IJCNN '11 (2011: Jul. 31-Aug. 5, San Jose, CA)
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1457710865
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2011
