NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data
Summary: NG-DBSCAN: an approximate, distributed DBSCAN variant for arbitrary data with any symmetric distance. It delivers scalable, fast clustering on large datasets with high-quality results, plus a detailed algorithmic walkthrough and extensive real/synthetic experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alessandro Lulli (Institute of Information Science and Technologies, National Research Council; University of Pisa)
- 2. Matteo Dell'Amico (Symantec Research Labs)
- 3. Pietro Michiardi (EURECOM)
- 4. Laura Ricci (Institute of Information Science and Technologies, National Research Council; University of Pisa)
BibTeX Citation
@article{lulli_vldb17,
title = {{NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data}},
author = {Lulli, Alessandro and Dell'Amico, Matteo and Michiardi, Pietro and Ricci, Laura},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {3},
pages = {157},
doi = {10.14778/3021924.3021932},
url = {https://doi.org/10.14778/3021924.3021932},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,234 | RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning | 2018 | SIGMOD | 7.6144184e-05 |
| 5,294 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD | 6.2790627e-05 |
| 5,483 | DenForest: Enabling Fast Deletion in Incremental Density-Based Clustering over Sliding Windows | 2022 | SIGMOD | 6.2019677e-05 |
| 6,956 | A New Sparse Data Clustering Method Based On Frequent Items | 2023 | SIGMOD | 5.7303405e-05 |
| 7,360 | On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection | 2021 | VLDB | 5.6340845e-05 |
| 8,070 | Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms | 2024 | SIGMOD | 5.4938502e-05 |
| 11,387 | Fast Density-Based Clustering: Geometric Approach | 2023 | SIGMOD | 5.093636e-05 |
| 11,664 | Fast Density-Peaks Clustering: Multicore-based Parallelization Approach | 2021 | SIGMOD | 5.093636e-05 |
| 11,675 | Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering* | 2021 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00012936472 |
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|---|---|---|---|---|
| 1 | 1,907 | Incremental Clustering for Mining in a Data Warehousing Environment | 1998 | VLDB |
| 2 | 11,664 | Fast Density-Peaks Clustering: Multicore-based Parallelization Approach | 2021 | SIGMOD |
| 3 | 3,234 | RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning | 2018 | SIGMOD |
| 4 | 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD |
| 5 | 7,500 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD |
| 6 | 3,020 | Dynamic Density Based Clustering | 2017 | SIGMOD |
| 7 | 5,294 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD |
| 8 | 11,387 | Fast Density-Based Clustering: Geometric Approach | 2023 | SIGMOD |
| 9 | 8,070 | Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms | 2024 | SIGMOD |
| 10 | 10,195 | Approximate DBSCAN via Density-Biased Sampling and Kernel Density Estimation | 2026 | SIGMOD |