Fast Density-Based Clustering Using Graphics Processing Units

Woong-Kee LOH; Yang-Sae MOON; Young-Ho PARK

doi:10.1587/transinf.E97.D.1349

Fast Density-Based Clustering Using Graphics Processing Units

Woong-Kee LOH, Yang-Sae MOON, Young-Ho PARK

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Summary :

Due to the recent technical advances, GPUs are used for general applications as well as screen display. Many research results have been proposed to the performance of previous CPU-based algorithms by a few hundred times using the GPUs. In this paper, we propose a density-based clustering algorithm called GSCAN, which reduces the number of unnecessary distance computations using a grid structure. As a result of our experiments, GSCAN outperformed CUDA-DClust [2] and DBSCAN [3] by up to 13.9 and 32.6 times, respectively.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.5 pp.1349-1352

Publication Date: 2014/05/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1349

Type of Manuscript: LETTER

Category: Artificial Intelligence, Data Mining

Authors

Woong-Kee LOH
  Sungkyul University
Yang-Sae MOON
  Kangwon National University
Young-Ho PARK
  Sookmyung Women's University

Keyword

density-based clustering, graphics processing units, grid structure

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Woong-Kee LOH, Yang-Sae MOON, Young-Ho PARK, "Fast Density-Based Clustering Using Graphics Processing Units" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 5, pp. 1349-1352, May 2014, doi: 10.1587/transinf.E97.D.1349.
Abstract: Due to the recent technical advances, GPUs are used for general applications as well as screen display. Many research results have been proposed to the performance of previous CPU-based algorithms by a few hundred times using the GPUs. In this paper, we propose a density-based clustering algorithm called GSCAN, which reduces the number of unnecessary distance computations using a grid structure. As a result of our experiments, GSCAN outperformed CUDA-DClust [2] and DBSCAN [3] by up to 13.9 and 32.6 times, respectively.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1349/_p

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@ARTICLE{e97-d_5_1349,
author={Woong-Kee LOH, Yang-Sae MOON, Young-Ho PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Fast Density-Based Clustering Using Graphics Processing Units},
year={2014},
volume={E97-D},
number={5},
pages={1349-1352},
abstract={Due to the recent technical advances, GPUs are used for general applications as well as screen display. Many research results have been proposed to the performance of previous CPU-based algorithms by a few hundred times using the GPUs. In this paper, we propose a density-based clustering algorithm called GSCAN, which reduces the number of unnecessary distance computations using a grid structure. As a result of our experiments, GSCAN outperformed CUDA-DClust [2] and DBSCAN [3] by up to 13.9 and 32.6 times, respectively.},
keywords={},
doi={10.1587/transinf.E97.D.1349},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Fast Density-Based Clustering Using Graphics Processing Units
T2 - IEICE TRANSACTIONS on Information
SP - 1349
EP - 1352
AU - Woong-Kee LOH
AU - Yang-Sae MOON
AU - Young-Ho PARK
PY - 2014
DO - 10.1587/transinf.E97.D.1349
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2014
AB - Due to the recent technical advances, GPUs are used for general applications as well as screen display. Many research results have been proposed to the performance of previous CPU-based algorithms by a few hundred times using the GPUs. In this paper, we propose a density-based clustering algorithm called GSCAN, which reduces the number of unnecessary distance computations using a grid structure. As a result of our experiments, GSCAN outperformed CUDA-DClust [2] and DBSCAN [3] by up to 13.9 and 32.6 times, respectively.
ER -