research-article

Fast Density-Based Clustering: Geometric Approach

Authors:

Xiaogang Huang,

Tiefeng MaAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 58, Pages 1 - 24

https://doi.org/10.1145/3588912

Published: 30 May 2023 Publication History

Abstract

DBSCAN is a fundamental density-based clustering algorithm with extensive applications. However, a bottleneck of DBSCAN is its O(n2) worst-case time complexity. In this paper, we propose an algorithm called GAP-DBC, which exploits the geometric relationships between points to solve this problem. GAP-DBC introduces an efficient partitioning algorithm to partition the data set with a limited number of range queries and then establishes an initial cluster structure based on the partition. GAP-DBC proceeds to iteratively refine the cluster structure by additional range queries. Finally, the cluster structure is accomplished using an iterative algorithm that utilizes the spatial relationships among points to reduce unnecessary distance calculations. We further demonstrate theoretically that GAP-DBC has an excellent guarantee in terms of computational efficiency. We conducted experiments on both synthetic and real-world data sets to evaluate the performance of GAP-DBC. The results show that our algorithm is competitive with other state-of-the-art algorithms.

Supplemental Material

MP4 File

Presentation video for SIGMOD 2023

Download
18.61 MB

References

[1]

Thapana Boonchoo, Xiang Ao, Yang Liu, Weizhong Zhao, Fuzhen Zhuang, and Qing He. 2019. Grid-based DBSCAN: Indexing and inference. Pattern Recognition, Vol. 90 (2019), 271--284.

Digital Library

[2]

Bhogeswar Borah and Dhruba K. Bhattacharyya. 2004. An improved sampling-based DBSCAN for large spatial databases. In Proceedings of the 2004 International Conference on Intelligent Sensing and Information Processing. IEEE, 92--96.

[3]

Yewang Chen, Shengyu Tang, Nizar Bouguila, Cheng Wang, Jixiang Du, and HaiLin Li. 2018. A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data. Pattern Recognition, Vol. 83 (2018), 375--387.

Digital Library

[4]

Yewang Chen, Lida Zhou, Nizar Bouguila, Cheng Wang, Yi Chen, and Jixiang Du. 2021. BLOCK-DBSCAN: Fast clustering for large scale data. Pattern Recognition, Vol. 109 (2021), 107624.

Digital Library

[5]

Yewang Chen, Lida Zhou, Songwen Pei, Zhiwen Yu, Yi Chen, Xin Liu, Jixiang Du, and Naixue Xiong. 2019. KNN-BLOCK DBSCAN: Fast clustering for large-scale data. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019).

[6]

Difei Cheng, Ruihang Xu, Bo Zhang, and Ruinan Jin. 2023. Fast Density Estimation for Density-based Clustering Methods. Neurocomputing (2023).

[7]

Dheeru Dua and Casey Graff. 2017. UCI machine learning repository. http://archive.ics.uci.edu/ml

[8]

Martin Ester, Hans-Peter Kriegel, Jö rg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD '96). 226--231.

Digital Library

[9]

Junhao Gan and Yufei Tao. 2015. DBSCAN revisited: Mis-claim, un-fixability, and approximation. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15). 519--530.

Digital Library

[10]

Junhao Gan and Yufei Tao. 2017. On the hardness and approximation of Euclidean DBSCAN. ACM Transactions on Database Systems (TODS), Vol. 42, 3 (2017), 1--45.

Digital Library

[11]

Teofilo F. Gonzalez. 1985. Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, Vol. 38 (1985), 293--306.

[12]

Ade Gunawan and M. de Berg. 2013. A faster algorithm for DBSCAN. Master's thesis (2013).

[13]

John A. Hartigan. 1975. Clustering algorithms. John Wiley & Sons.

Digital Library

[14]

Xiaogang Huang, Tiefeng Ma, Conan Liu, and Shuangzhe Liu. 2022. GriT-DBSCAN: A spatial clustering algorithm for very large databases. arXiv preprint arXiv:2210.07580 (2022).

[15]

Jennifer Jang and Heinrich Jiang. 2019. DBSCAN: Towards fast and scalable density clustering. In International Conference on Machine Learning. PMLR, 3019--3029.

[16]

Heinrich Jiang, Jennifer Jang, and Jakub Lacki. 2020. Faster DBSCAN via subsampled similarity queries. In Advances in Neural Information Processing Systems, Vol. 33. 22407--22419.

[17]

Robert Krauthgamer and James R. Lee. 2004. Navigating nets: Simple algorithms for proximity search. In Proceedings of the Fifteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '04). USA, 798--807.

Digital Library

[18]

K. Mahesh Kumar and A. Rama Mohan Reddy. 2016. A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recognition, Vol. 58 (2016), 39--48.

Digital Library

[19]

Bing Liu. 2006. A fast density-based clustering algorithm for large databases. In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 996--1000.

[20]

Alessandro Lulli, Matteo Dell'Amico, Pietro Michiardi, and Laura Ricci. 2016. NG-DBSCAN: Scalable density-based clustering for arbitrary data. Proceedings of the VLDB Endowment, Vol. 10, 3 (2016), 157--168.

Digital Library

[21]

Shaaban Mahran and Khaled Mahar. 2008. Using grid for accelerating density-based clustering. In 8th IEEE International Conference on Computer and Information Technology. IEEE, 35--40.

[22]

Son T. Mai, Ira Assent, and Martin Storgaard. 2016. AnyDBC: An efficient anytime density-based clustering algorithm for very large complex datasets. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). 1025--1034.

Digital Library

[23]

Son T. Mai, Jon Jacobsen, Sihem Amer-Yahia, Ivor Spence, Nhat-Phuong Tran, Ira Assent, and Quoc Viet Hung Nguyen. 2022. Incremental density-based clustering on multicore processors. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 3 (2022), 1338--1356.

[24]

Md Mostofa Ali Patwary, Diana Palsetia, Ankit Agrawal, Wei-keng Liao, Fredrik Manne, and Alok Choudhary. 2012. A new scalable parallel DBSCAN algorithm using the disjoint-set data structure. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC'12). 1--11.

[25]

Aditya Sarma, Poonam Goyal, Sonal Kumari, Anand Wani, Jagat Sesh Challa, Saiyedul Islam, and Navneet Goyal. 2019. μDBSCAN: an exact scalable DBSCAN algorithm for big data exploiting spatial locality. In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 1--11.

[26]

Erich Schubert, Jörg Sander, Martin Ester, Hans Peter Kriegel, and Xiaowei Xu. 2017. DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), Vol. 42, 3 (2017), 1--21.

Digital Library

[27]

Hwanjun Song and Jae-Gil Lee. 2018. RP-DBSCAN: A superfast parallel DBSCAN algorithm based on random partitioning. In Proceedings of the 2018 International Conference on Management of Data. 1173--1187.

Digital Library

[28]

Robert Endre Tarjan. 1979. A class of algorithms which require nonlinear time to maintain disjoint sets. Journal of computer and system sciences, Vol. 18, 2 (1979), 110--127.

[29]

Isaac Todhunter. 1863. Spherical trigonometry, for the use of colleges and schools: with numerous examples. Macmillan.

[30]

Manik Varma and Andrew Zisserman. 2003. Texture classification: Are filter banks necessary?. In 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., Vol. 2. IEEE, II--691.

[31]

P. Viswanath and V. Suresh Babu. 2009. Rough-DBSCAN: A fast hybrid density based clustering method for large data sets. Pattern Recognition Letters, Vol. 30, 16 (2009), 1477--1488.

Digital Library

[32]

P. Viswanath and R. Pinkesh. 2006. l-DBSCAN: A fast hybrid density based clustering method. In 18th International Conference on Pattern Recognition (ICPR '06), Vol. 1. IEEE, 912--915.

[33]

Yiqiu Wang, Yan Gu, and Julian Shun. 2020. Theoretically-efficient and practical parallel DBSCAN. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20). 2555--2571.

Digital Library

[34]

Guoqing Wu, Liqiang Cao, Hongyun Tian, and Wei Wang. 2022. HY-DBSCAN: A hybrid parallel DBSCAN clustering algorithm scalable on distributed-memory computers. J. Parallel and Distrib. Comput. (2022).

[35]

Shuigeng Zhou, Aoying Zhou, Jing Cao, Jin Wen, Ye Fan, and Yunfa Hu. 2000. Combining sampling technique with DBSCAN algorithm for clustering large spatial databases. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 169--172.

Cited By

Bača R(2024)Window Function Expression: Let the Self-Join EnterProceedings of the VLDB Endowment10.14778/3665844.366584817:9(2162-2174)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665848
Yan YWong R(2024)Proximity Queries on Point Clouds using Rapid Construction Path OracleProceedings of the ACM on Management of Data10.1145/36392612:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639261

Index Terms

Fast Density-Based Clustering: Geometric Approach
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

Recommendations

FINEX: A Fast Index for Exact & Flexible Density-Based Clustering
PACMMOD

Density-based clustering aims to find groups of similar objects (i.e., clusters) in a given dataset. Applications include, e.g., process mining and anomaly detection. It comes with two user parameters (ε, MinPts) that determine the clustering result, but ...
A new hybrid method based on partitioning-based DBSCAN and ant clustering

Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based ...
Density-based semi-supervised clustering

Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

Issue’s Table of Contents

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Author Tags

Qualifiers

Research-article

Funding Sources

Academic Exchange Program for Doctoral Students Abroad of Southwestern University of Finance and Economics

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
211
Total Downloads

Downloads (Last 12 months)141
Downloads (Last 6 weeks)8

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bača R(2024)Window Function Expression: Let the Self-Join EnterProceedings of the VLDB Endowment10.14778/3665844.366584817:9(2162-2174)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665848
Yan YWong R(2024)Proximity Queries on Point Clouds using Rapid Construction Path OracleProceedings of the ACM on Management of Data10.1145/36392612:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639261

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents