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
- 2. Shihong Song
- 3. Hu Ding
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,480 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD | 4.1905499e-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 |
|---|---|---|---|---|
| 263 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00029955858 |
| 918 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00015286593 |
| 2,669 | NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data | 2017 | VLDB | 8.3429208e-05 |
| 3,296 | RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning | 2018 | SIGMOD | 7.2553154e-05 |
| 4,077 | Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially | 2019 | VLDB | 6.4634641e-05 |
| 5,417 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD | 5.5162242e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,078 | Dynamic Density Based Clustering | 2017 | SIGMOD | 7.6041723e-05 |
| 5,315 | Clustering Stream Data by Exploring the Evolution of Density Mountain | 2018 | VLDB | 5.5702177e-05 |
| 1,803 | Incremental Clustering for Mining in a Data Warehousing Environment | 1998 | VLDB | 0.00010481475 |
| 2,669 | NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data | 2017 | VLDB | 8.3429208e-05 |
| 9,923 | On Saving Outliers for Better Clustering over Noisy Data | 2021 | SIGMOD | 4.2503475e-05 |
| 10,480 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD | 4.1905499e-05 |
| 4,077 | Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially | 2019 | VLDB | 6.4634641e-05 |
| 11,184 | Fast Density-Based Clustering: Geometric Approach | 2023 | SIGMOD | 4.1905499e-05 |
| 5,417 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD | 5.5162242e-05 |
| 918 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00015286593 |