HNSW-based DBSCAN for Large-scale Point Cloud Computing

Jiwan Park; Seongjin Oh; Tae Yong Kim; Jieun Lee; Jaebin Lee; Jongpil Jeong

doi:10.1109/BCD57833.2023.10466352

HNSW-based DBSCAN for Large-scale Point Cloud Computing

Jiwan Park
, Seongjin Oh
, Tae Yong Kim
, Jieun Lee
, Jaebin Lee
, Jongpil Jeong

Research & Business Foundation

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research aims to introduce an innovative algorithm for processing point cloud data, a key technology in the Fourth Industrial Revolution. Point cloud data, which is used to accurately represent real-world objects and environments, is often noisy due to environmental factors, which affects the accuracy and reliability of the data. The proposed algorithm effectively solves the noise problem to achieve high accuracy in data analysis and information extraction. The framework also combines the HNSW (Hierarchical Navigable Small World) index and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm for efficient data retrieval and management in large data sets. These tools can be applied to various domains and have demonstrated robust performance in experiments. Therefore, this research has the potential to drive innovation in the Fourth Industrial Revolution and is expected to serve as a practical tool for accurate data analysis and information extraction from large data sets.

Original language	English
Title of host publication	2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023
Editors	Jongwoo Park, Ngo Thi Phuong Lan, Sungtaek Lee, Tran Anh Tien, Jongbae Kim
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	86-91
Number of pages	6
ISBN (Electronic)	9798350373615
DOIs	https://doi.org/10.1109/BCD57833.2023.10466352
State	Published - 2023
Event	8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 - Ho Chi Minh City, Viet Nam Duration: 14 Dec 2023 → 16 Dec 2023

Publication series

Name	2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

Conference

Conference	8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023
Country/Territory	Viet Nam
City	Ho Chi Minh City
Period	14/12/23 → 16/12/23

Keywords

Clustering
Denosing
Large-scale data
Nearest Neighbor
Point cloud

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BCD57833.2023.10466352

Cite this

Park, J., Oh, S., Kim, T. Y., Lee, J., Lee, J., & Jeong, J. (2023). HNSW-based DBSCAN for Large-scale Point Cloud Computing. In J. Park, N. T. P. Lan, S. Lee, T. A. Tien, & J. Kim (Eds.), 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 (pp. 86-91). (2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCD57833.2023.10466352

Park, Jiwan ; Oh, Seongjin ; Kim, Tae Yong et al. / HNSW-based DBSCAN for Large-scale Point Cloud Computing. 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023. editor / Jongwoo Park ; Ngo Thi Phuong Lan ; Sungtaek Lee ; Tran Anh Tien ; Jongbae Kim. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 86-91 (2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023).

@inproceedings{d48fbcc1b372454ba95d8a34ca47c443,

title = "HNSW-based DBSCAN for Large-scale Point Cloud Computing",

abstract = "This research aims to introduce an innovative algorithm for processing point cloud data, a key technology in the Fourth Industrial Revolution. Point cloud data, which is used to accurately represent real-world objects and environments, is often noisy due to environmental factors, which affects the accuracy and reliability of the data. The proposed algorithm effectively solves the noise problem to achieve high accuracy in data analysis and information extraction. The framework also combines the HNSW (Hierarchical Navigable Small World) index and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm for efficient data retrieval and management in large data sets. These tools can be applied to various domains and have demonstrated robust performance in experiments. Therefore, this research has the potential to drive innovation in the Fourth Industrial Revolution and is expected to serve as a practical tool for accurate data analysis and information extraction from large data sets.",

keywords = "Clustering, Denosing, Large-scale data, Nearest Neighbor, Point cloud",

author = "Jiwan Park and Seongjin Oh and Kim, \{Tae Yong\} and Jieun Lee and Jaebin Lee and Jongpil Jeong",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 ; Conference date: 14-12-2023 Through 16-12-2023",

year = "2023",

doi = "10.1109/BCD57833.2023.10466352",

language = "English",

series = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "86--91",

editor = "Jongwoo Park and Lan, \{Ngo Thi Phuong\} and Sungtaek Lee and Tien, \{Tran Anh\} and Jongbae Kim",

booktitle = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",

}

Park, J, Oh, S, Kim, TY, Lee, J, Lee, J & Jeong, J 2023, HNSW-based DBSCAN for Large-scale Point Cloud Computing. in J Park, NTP Lan, S Lee, TA Tien & J Kim (eds), 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023. 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023, Institute of Electrical and Electronics Engineers Inc., pp. 86-91, 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023, Ho Chi Minh City, Viet Nam, 14/12/23. https://doi.org/10.1109/BCD57833.2023.10466352

HNSW-based DBSCAN for Large-scale Point Cloud Computing. / Park, Jiwan; Oh, Seongjin; Kim, Tae Yong et al.
2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023. ed. / Jongwoo Park; Ngo Thi Phuong Lan; Sungtaek Lee; Tran Anh Tien; Jongbae Kim. Institute of Electrical and Electronics Engineers Inc., 2023. p. 86-91 (2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - HNSW-based DBSCAN for Large-scale Point Cloud Computing

AU - Park, Jiwan

AU - Oh, Seongjin

AU - Kim, Tae Yong

AU - Lee, Jieun

AU - Lee, Jaebin

AU - Jeong, Jongpil

PY - 2023

Y1 - 2023

N2 - This research aims to introduce an innovative algorithm for processing point cloud data, a key technology in the Fourth Industrial Revolution. Point cloud data, which is used to accurately represent real-world objects and environments, is often noisy due to environmental factors, which affects the accuracy and reliability of the data. The proposed algorithm effectively solves the noise problem to achieve high accuracy in data analysis and information extraction. The framework also combines the HNSW (Hierarchical Navigable Small World) index and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm for efficient data retrieval and management in large data sets. These tools can be applied to various domains and have demonstrated robust performance in experiments. Therefore, this research has the potential to drive innovation in the Fourth Industrial Revolution and is expected to serve as a practical tool for accurate data analysis and information extraction from large data sets.

AB - This research aims to introduce an innovative algorithm for processing point cloud data, a key technology in the Fourth Industrial Revolution. Point cloud data, which is used to accurately represent real-world objects and environments, is often noisy due to environmental factors, which affects the accuracy and reliability of the data. The proposed algorithm effectively solves the noise problem to achieve high accuracy in data analysis and information extraction. The framework also combines the HNSW (Hierarchical Navigable Small World) index and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm for efficient data retrieval and management in large data sets. These tools can be applied to various domains and have demonstrated robust performance in experiments. Therefore, this research has the potential to drive innovation in the Fourth Industrial Revolution and is expected to serve as a practical tool for accurate data analysis and information extraction from large data sets.

KW - Clustering

KW - Denosing

KW - Large-scale data

KW - Nearest Neighbor

KW - Point cloud

UR - https://www.scopus.com/pages/publications/85189943219

U2 - 10.1109/BCD57833.2023.10466352

DO - 10.1109/BCD57833.2023.10466352

M3 - Conference contribution

AN - SCOPUS:85189943219

T3 - 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

SP - 86

EP - 91

BT - 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

A2 - Park, Jongwoo

A2 - Lan, Ngo Thi Phuong

A2 - Lee, Sungtaek

A2 - Tien, Tran Anh

A2 - Kim, Jongbae

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

Y2 - 14 December 2023 through 16 December 2023

ER -

Park J, Oh S, Kim TY, Lee J, Lee J, Jeong J. HNSW-based DBSCAN for Large-scale Point Cloud Computing. In Park J, Lan NTP, Lee S, Tien TA, Kim J, editors, 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 86-91. (2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023). doi: 10.1109/BCD57833.2023.10466352

HNSW-based DBSCAN for Large-scale Point Cloud Computing

Abstract

Publication series

Conference

Keywords

UN SDGs

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