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Fast Euclidean OPTICS with Bounded Precision in Low Dimensional Space

Summary: Introduces a fast, bounded-precision Euclidean OPTICS for fixed-dimensional data, replacing exact O(n^2) with approximations that have provable discrepancy guarantees. Runs in O(n log n) time, yields a linear-space index enabling near-optimal cluster-group-by queries, with empirical validation on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5630
Venue
SIGMOD
Year
2018
Pagerank
6.0613257e-05
Overall Rank
5,867 | 59.75%
DOI
10.1145/3183713.3196922

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gan_sigmod18,
        title = {{Fast Euclidean OPTICS with Bounded Precision in Low Dimensional Space}},
        author = {Gan, Junhao and Tao, Yufei},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3196922},
        url = {https://dl.acm.org/doi/10.1145/3183713.3196922},
        year = {2018}
}

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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
3,020 Dynamic Density Based Clustering 2017 SIGMOD 7.8445412e-05
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