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Advancements of Outlier Detection: A Survey [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2013 Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements
Ji Zhang
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Outlier detection algorithm based on k-nearest neighbors-local outlier factor
Journal of Algorithms & Computational Technology, 2022 The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the
He Xu, Lin Zhang, Peng Li, Feng Zhu
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Progress in Outlier Detection Techniques: A Survey
IEEE Access, 2019 Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and
Hongzhi Wang +2 more
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IEEE Access, 2017 In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the ...
Xiaodong Zhu +5 more
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Investigation of outlier detection algorithm
Lietuvos Matematikos Rinkinys, 2005 The proposed outlier factor was used to analyze the multidimensional data sets regarding outlier detection. The paper describes two kinds of investigation: the influence of omitting some part of distances between data points, and the influence of ...
Vydūnas Šaltenis
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, 2020 An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed towards particular groups defined on such sensitive attributes.
Savitha Sam Abraham +3 more
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, 2013 Outlier detection aims to capture or identify uncommon events or instances. This technique has been widely used in applications such as fraud detection, image processing and bioinformatics. Because of its diverse usage, outlier detection has emerged as a
Li, Shukai.
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Locality and Consistency Based Sequential Ensemble Method for Outlier Detection [PDF]
Jisuanji kexue, 2022 Outlier detection has been widely used in many fields,such as network intrusion detection,credit card fraud detection,etc.The increase in data dimensions leads to many irrelevant and redundant features,which will obscure the relevant features and result ...
LIU Yi, MAO Ying-chi, CHENG Yang-kun, GAO Jian, WANG Long-bao
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FairLOF: Fairness in Outlier Detection [PDF]
Data Science and Engineering, 2021 AbstractAn outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed toward particular groups defined on such sensitive attributes. In this paper, we consider the task of fair outlier detection.
Deepak P 0001, Savitha Sam Abraham
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A novel subspace outlier detection method by entropy-based clustering algorithm
Scientific Reports, 2023 Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the “curse of dimensionality”. Subspace outlier detection methods have
Zheng Zuo +3 more
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