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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
6064
Venue
SIGMOD
Year
2021
Pagerank
4.1905499e-05
Overall Rank
11,470 | 20.29%
DOI
10.1145/3448016.3452782

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Showing 9 of 9 cited papers.

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

Rank Cited Paper Year Venue Pagerank
210 Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph 2019 VLDB 0.00034086264
768 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016864875
1,859 Distance-based Outlier Detection in Data Streams 2016 VLDB 0.00010307902
3,014 NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing 2019 VLDB 7.7079954e-05
3,472 Real-Time Distance-Based Outlier Detection in Data Streams 2021 VLDB 7.062401e-05
5,086 Pivot-based Metric Indexing 2017 VLDB 5.7036022e-05
6,990 Sharing-Aware Outlier Analytics over High-Volume Data Streams 2016 SIGMOD 4.8656085e-05
7,765 Cache-oblivious High-performance Similarity Join 2019 SIGMOD 4.6527424e-05
11,469 Fast Density-Peaks Clustering: Multicore-based Parallelization Approach 2021 SIGMOD 4.1905499e-05
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