DBScholar

Back to papers

Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach

Summary: MRPG proximity-graph approach for exact distance-based outlier detection in metric spaces. Supports arbitrary proximity graphs, enabling a main-memory index and significant speedups over the state-of-the-art on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6125
Venue
SIGMOD
Year
2021
Pagerank
5.2434488e-05
Overall Rank
9,634 | 33.91%
DOI
10.1145/3448016.3452782

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{amagata_sigmod21,
        title = {{Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach}},
        author = {Amagata, Daichi and Onizuka, Makoto and Hara, Takahiro},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452782},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452782},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,189 Adaptive Outlier Detection over Data Stream 2026 SIGMOD 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers