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A novel subspace outlier detection method by entropy-based clustering algorithm [PDF]
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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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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Fluctuation-based outlier detection [PDF]
Scientific Reports, 2023 Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects.
Xusheng Du +4 more
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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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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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Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
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Applied Sciences, 2023 A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc.
Yuehua Huang +4 more
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A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams
Big Data and Cognitive Computing, 2020 Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning.
Omar Alghushairy +3 more
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Attribute Grouping-based Categorical Outlier Detection Using Isolation Forest Ensemble Strategy [PDF]
Jisuanji kexueAttribute grouping is one of the effective steps in high-dimensional outlier detection,but the current ensemble strategies in attribute grouping-based outlier detection only take into account the local outlier information within each attribute group,and ...
SONG Yijing, ZHANG Jifu
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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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