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Outlier Detection Based on the Distribut ...
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Outlier Detection Based on the Distribution of Distances between Data Points
Teleophthalmology in Lithuania
Alvydas Paunksnis, Valerijus Barzdžiukas, Skaidra Kurapkienė
https://doi.org/10.1007/3-540-33714-8_22
Book:
(2006), p. 167
REFERENCES
https://doi.org/10.1002/9781119606383.refs
Book:
(2021), p. 185
Surprise Detection in Multivariate Astronomical Data
Kirk D. Borne, Arun Vedachalam
https://doi.org/10.1007/978-1-4614-3520-4_26
Book:
Lecture Notes in Statistics (Statistical Challenges in Modern Astronomy V)
Volume 902 (2012), p. 275
An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems
Abdulmohsen Almalawi, Adil Fahad, Zahir Tari, Abdullah Alamri, Rayed AlGhamdi, Albert Y. Zomaya
https://doi.org/10.1109/TIFS.2015.2512522
Journal:
IEEE Transactions on Information Forensics and Security
Volume 11, Issue 5 (2016), p. 893
An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems
Abdulmohsen Almalawi, Xinghuo Yu, Zahir Tari, Adil Fahad, Ibrahim Khalil
https://doi.org/10.1016/j.cose.2014.07.005
Journal:
Computers & Security
Volume 46 (2014), p. 94
Data Clustering Based on Maximization of Outlier Factor
Vydunas Saltenis
https://doi.org/10.1007/s10898-005-5372-5
Journal:
Journal of Global Optimization
Volume 35, Issue 4 (2006), p. 625
Managing variability in the summary and comparison of gait data
Tom Chau, Scott Young, Sue Redekop
https://doi.org/10.1186/1743-0003-2-22
Journal:
Journal of NeuroEngineering and Rehabilitation
Volume 2, Issue 1 (2005)
Monte Carlo method for identification of outlier molecules in QSAR studies
Tarko Laszlo
https://doi.org/10.1007/s10910-009-9540-6
Journal:
Journal of Mathematical Chemistry
Volume 47, Issue 1 (2010), p. 174
Novel criteria for elimination of the outliers in QSPR studies, when the ‘forward stepwise’ procedure is used
Laszlo Tarko
https://doi.org/10.1007/s10910-019-01036-x
Journal:
Journal of Mathematical Chemistry
Volume 57, Issue 7 (2019), p. 1770
Statistical evaluation of spectral methods for anomaly detection in static networks
Tomilayo Komolafe, A. Valeria Quevedo, Srijan Sengupta, William H. Woodall
https://doi.org/10.1017/nws.2019.14
Journal:
Network Science
Volume 7, Issue 3 (2019), p. 319
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