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OIE: An Interpretable System for Outlier Explanation and Summarization

Summary: OIE is an interpretable system for outlier explanation and summarization, delivering fine-grained, rule-based insights instead of opaque flags. It uses decision trees to generate concise rules, with dynamic data partitioning and a boundary stabilizer to scale to high-dimensional data and subspace-specific outlier causes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7169
Venue
SIGMOD
Year
2025
Pagerank
4.3341665e-05
Overall Rank
9,475 | 34.09%
DOI
10.1145/3722212.3725120

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Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,984 Towards Scalable Visual Data Wrangling via Direct Manipulation 2026 CIDR 4.1945683e-05
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Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
214 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.0003363692
9,709 Outlier Summarization via Human Interpretable Rules 2024 VLDB 4.299267e-05
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