ArticleThe VLDB journal : very large data bases : a publication of the VLDB Endowment2022
A survey on outlier explanations.
Article in The VLDB journal : very large data bases : a publication of the VLDB Endowment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Health workers' perspectives on the quality of maternal and newborn health care around the time of childbirth: Results of the Improving MAternal Newborn carE in the EURO Region (IMAgiNE EURO) project in 12 countries of the World Health Organization European Region.Journal of global health · 2024Trial
- Artificial intelligence-based modeling and validation for prediction of drug delivery capacity and cytotoxicity in design of porous materials.Frontiers in chemistry · 2026Article
- Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Batch production prediction for the mechanical cutting industry based on process capability.Scientific reports · 2024Article
- Feature relevance XAI in anomaly detection: Reviewing approaches and challenges.Frontiers in artificial intelligence · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While many techniques for outlier detection have been proposed in the literature, the interpretation of detected outliers is often left to users. As a result, it is difficult for users to promptly take appropriate actions concerning the detected outliers. To lessen this difficulty, when outliers are identified, they should be presented together with their explanations. There are survey papers on outlier detection, but none exists for outlier explanations. To fill this gap, in this paper, we present a survey on outlier explanations in which meaningful knowledge is mined from anomalous data to explain them. We define different types of outlier explanations and discuss the challenges in generating each type. We review the existing outlier explanation techniques and discuss how they address the challenges. We also discuss the applications of outlier explanations and review the existing methods used to evaluate outlier explanations. Furthermore, we discuss possible future research directions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.