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.

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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

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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

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Copyright © 2023 ACM.

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Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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

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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

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