ReviewInternational journal of data science and analytics2021
On the nature and types of anomalies: a review of deviations in data.
Review in International journal of data science and analytics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Data for Machine Learning Models: Clinical Experience Determines Interrater Reliability in Waveform Anomaly Detection.Neurocritical care · 2026Article
- Variations in ray flower numbers of Common Daisy (Bellis perennis L.) - the hidden cues of the Fibonacci order.PloS one · 2026Article
- Anomaly detection in urban lighting systems using autoencoder and transformer algorithms.Scientific reports · 2025Article
- Outliers and anomalies in training and testing datasets for AI-powered morphometry-evidence from CT scans of the spleen.Frontiers in artificial intelligence · 2025Article
- Predictive model for totally implanted venous access ports‑related long‑term complications in patients with lung cancer.Oncology letters · 2024Article
- Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach.BMC medical research methodology · 2024Article
- An anomaly detection method for identifying locations with abnormal behavior of temperature in school buildings.Scientific reports · 2023Article
- Article
- Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects.Sensors (Basel, Switzerland) · 2022Review
- Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of publications on the topic, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations, the typology employs five dimensions: data type, cardinality of relationship, anomaly level, data structure, and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types, and 63 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.
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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.