Results 31 to 40 of about 4,573,641 (310)

A Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks [PDF]

IEEE Transactions on Industrial Informatics, 2021
Security is one of the major concerns in industrial wireless sensor networks (IWSNs). To assure the security in clustered IWSNs, this article presents a secure clustering protocol with fuzzy trust evaluation and outlier detection. First, to deal with the
Liu Yang, Yinzhi Lu, Simon X. Yang, Tan Guo, Zhifang Liang   +4 more
semanticscholar   +1 more source

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
doaj   +3 more sources

Detecting Outliers in Non-IID Data: A Systematic Literature Review

IEEE Access, 2023
Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and ...
Shafaq Siddiqi, Faiza Qureshi, Stefanie Lindstaedt, Roman Kern   +3 more
doaj   +1 more source

Outlier Edge Detection Using Random Graph Generation Models and Applications [PDF]

, 2016
Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users ...
A Lancichinetti, AK Jain, DJ Watts, G Karypis, H Zhang, J Leskovec, J Shi, J Yang, L Akoglu, L Danon, L Danon, L Liu, L Lu, L Waltman, LC Freeman, M Choudhury De, M Coscia, M Newman, M Rosvall, ME Newman, ME Newman, MEJ Newman, MR Brito, R Yu, S Fortunato, S Lloyd, S Papadopoulos, SE Schaeffer, VD Blondel, VJ Hodge, X Dong   +30 more
core   +3 more sources

Mean-shift outlier detection and filtering

Pattern Recognition, 2021
Traditional outlier detection methods create a model for data and then label as outliers for objects that deviate significantly from this model. However, when dat has many outliers, outliers also pollute the model.
Jiawei Yang, S. Rahardja, P. Fränti
semanticscholar   +1 more source

Review of Outlier Detection Algorithms [PDF]

Jisuanji kexue
Outlier detection,as an important research direction in the field of data mining,aims to discover data points in a dataset that are different from the majority and have potential analytical value,assistresearchers in identifying potential issues in the ...
KONG Lingchao, LIU Guozhu
doaj   +1 more source

A Comparison of Outlier Detection Techniques for High-Dimensional Data

International Journal of Computational Intelligence Systems, 2018
Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly.
Xiaodan Xu, Huawen Liu, Li Li, Minghai Yao   +3 more
doaj   +1 more source

A Novel Outlier Detection Model for Vibration Signals Using Transformer Networks

IEEE Access, 2022
Outlier detection in vibration signals can play an important role in addressing the issue of structural or environmental changes during vibration testing. In this study, a transformer-based model for outlier detection is proposed.
Ruiheng Zhang, Quan Zhou, Lulu Tian, Libing Bai, Jie Zhang   +4 more
doaj   +1 more source

Deep Clustering based Fair Outlier Detection [PDF]

Knowledge Discovery and Data Mining, 2021
In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitly encode and propagate statistical bias in data and raise societal ...
Hanyu Song, Peizhao Li, Hongfu Liu
semanticscholar   +1 more source

An Incremental Local Outlier Detection Method in the Data Stream

Applied Sciences, 2018
Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao, Xiuwen Fu, Yongsheng Yang, Octavian Postolache   +3 more
doaj   +1 more source