Approximate DBSCAN via Density-Biased Sampling and Kernel Density Estimation
Summary: Reformulates DBSCAN as a Minimum Connected Dominating Set problem. LDBS-KDE combines lattice-based density-biased sampling with KDE-based core detection for substantially faster approximation, achieving competitive or superior accuracy. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Jian Lin (Shenzhen University)
- 2. Siyue Wu (Shenzhen University)
- 3. Dingming Wu (Shenzhen University)
- 4. Tsz Nam Chan (Shenzhen University)
BibTeX Citation
@inproceedings{lin_sigmod26,
title = {{Approximate DBSCAN via Density-Biased Sampling and Kernel Density Estimation}},
author = {Lin, Jian and Wu, Siyue and Wu, Dingming and Chan, Tsz Nam},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802013},
url = {https://dl.acm.org/doi/10.1145/3802013},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00012936472 |
| 2,638 | Quality and Efficiency in Kernel Density Estimates for Large Data | 2013 | SIGMOD | 8.3130624e-05 |
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