A GPU Based Parallel Hierarchical Fuzzy ART Clustering

Sejun Kim; Donald C. Wunsch

doi:10.1109/IJCNN.2011.6033584

A GPU Based Parallel Hierarchical Fuzzy ART Clustering

Sejun Kim
, Donald C. Wunsch

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Proceedings of the International Joint Conference on Neural Networks
DOIs	https://doi.org/10.1109/IJCNN.2011.6033584
State	Published - Jan 1 2011

Disciplines

Electrical and Computer Engineering

Access to Document

10.1109/IJCNN.2011.6033584License: Unspecified

https://doi.org/10.1109/IJCNN.2011.6033584

Cite this

@article{63d5d16a293045548b6b2412e10421d1,

title = "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.",

author = "Sejun Kim and Wunsch, \{Donald C.\}",

year = "2011",

month = jan,

day = "1",

doi = "10.1109/IJCNN.2011.6033584",

language = "American English",

journal = "Proceedings of the International Joint Conference on Neural Networks",

issn = "2161-4407",

}

TY - JOUR

T1 - A GPU Based Parallel Hierarchical Fuzzy ART Clustering

AU - Kim, Sejun

AU - Wunsch, Donald C.

PY - 2011/1/1

Y1 - 2011/1/1

N2 - 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.

AB - 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.

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/465

UR - https://doi.org/10.1109/IJCNN.2011.6033584

U2 - 10.1109/IJCNN.2011.6033584

DO - 10.1109/IJCNN.2011.6033584

M3 - Article

SN - 2161-4407

JO - Proceedings of the International Joint Conference on Neural Networks

JF - Proceedings of the International Joint Conference on Neural Networks

ER -

A GPU Based Parallel Hierarchical Fuzzy ART Clustering

Abstract

Disciplines

Access to Document

Other files and links

Fingerprint

Cite this