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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
6063
Venue
SIGMOD
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,467 | 20.23%
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
212 Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph 2019 VLDB 0.00033913475
774 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016865771
1,854 Distance-based Outlier Detection in Data Streams 2016 VLDB 0.00010317762
3,012 NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing 2019 VLDB 7.7153586e-05
3,468 Real-Time Distance-Based Outlier Detection in Data Streams 2021 VLDB 7.0686044e-05
4,985 Pivot-based Metric Indexing 2017 VLDB 5.7856648e-05
6,991 Sharing-Aware Outlier Analytics over High-Volume Data Streams 2016 SIGMOD 4.8702811e-05
7,765 Cache-oblivious High-performance Similarity Join 2019 SIGMOD 4.6572085e-05
11,466 Fast Density-Peaks Clustering: Multicore-based Parallelization Approach 2021 SIGMOD 4.1945683e-05
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