Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms
Summary: Metric-space DBSCAN beyond Euclidean/low-d: assumes inliers have low intrinsic dimension, leaving outliers arbitrary, to cut labeling/merging to near-linear via k-center ideas. Also gives linear-time approximation and a streaming summary with memory independent of input size. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Guanlin Mo (University of Science and Technology Beijing)
- 2. Shihong Song (University of Science and Technology Beijing)
- 3. Hu Ding (University of Science and Technology Beijing)
BibTeX Citation
@inproceedings{mo_sigmod24,
title = {{Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms}},
author = {Mo, Guanlin and Song, Shihong and Ding, Hu},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654981},
url = {https://dl.acm.org/doi/10.1145/3654981},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,500 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD | 5.6029996e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 291 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00022264197 |
| 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00012936472 |
| 2,556 | NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data | 2017 | VLDB | 8.4162172e-05 |
| 3,234 | RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning | 2018 | SIGMOD | 7.6144184e-05 |
| 4,366 | Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially | 2019 | VLDB | 6.7407122e-05 |
| 5,294 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD | 6.2790627e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,335 | Clustering Stream Data by Exploring the Evolution of Density Mountain | 2018 | VLDB |
| 2 | 1,907 | Incremental Clustering for Mining in a Data Warehousing Environment | 1998 | VLDB |
| 3 | 2,556 | NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data | 2017 | VLDB |
| 4 | 10,065 | On Saving Outliers for Better Clustering over Noisy Data | 2021 | SIGMOD |
| 5 | 7,500 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD |
| 6 | 4,366 | Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially | 2019 | VLDB |
| 7 | 11,387 | Fast Density-Based Clustering: Geometric Approach | 2023 | SIGMOD |
| 8 | 10,195 | Approximate DBSCAN via Density-Biased Sampling and Kernel Density Estimation | 2026 | SIGMOD |
| 9 | 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD |
| 10 | 5,294 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD |