Fast Density-Based Clustering: Geometric Approach
Summary: GAP-DBC leverages geometric relations to beat DBSCAN’s O(n^2) bottleneck via a partition-based prestructure built from a limited set of range queries. Iterative refinement with spatial pruning reduces distance calculations, backed by theoretical guarantees and competitive empirical results. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xiaogang Huang
- 2. Tiefeng Ma
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 |
| 5,417 | Theoretically-Efficient and Practical Parallel DBSCAN | 2020 | SIGMOD | 5.5162242e-05 |
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