A GPU based Parallel Hierarchical Fuzzy ART clustering

Sejun Kim, D. Wunsch
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引用次数: 20

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.
基于GPU的并行层次模糊ART聚类
分层聚类是一种重要而强大但计算量大的操作。它的复杂性促使人们探索高度并行的方法,如自适应共振理论(ART)。虽然ART已经在GPU处理器上实现,但本文提出了我们所知道的第一个分层ART GPU实现。每个ART层分布在GPU的多处理器中,并同时进行训练。实验结果表明,对于深度树,GPU的性能优势是显著的。
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