Results 21 to 30 of about 258,865 (274)

Online Bivariate Outlier Detection in Final Test Using Kernel Density Estimation [PDF]

, 2011
In parametric IC testing, outlier detection is applied to filter out potential unreliable devices. Most outlier detection methods are used in an offline setting and hence are not applicable to Final Test, where immediate pass/fail decisions are required.
Bossers, H.C.M., Hurink, J.L., Smit, G.J.M.   +2 more
core   +16 more sources

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
doaj   +1 more source

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, Enrico Weigelt, Dalibor Fiala, Martin Schramm   +3 more
doaj   +1 more source

Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients

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, Wenfen Liu, Song Li, Ying Guo, Wen Chen   +4 more
doaj   +1 more source

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, Raed Alsini, Terence Soule, Xiaogang Ma   +3 more
doaj   +1 more source

Attribute Grouping-based Categorical Outlier Detection Using Isolation Forest Ensemble Strategy [PDF]

Jisuanji kexue
Attribute 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
doaj   +1 more source

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

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

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