Surviving the Squeeze: Genomic Analysis of a Successful Invasion by European Common Wall Lizards (Podarcis muralis) in North America (Ohio, USA). [PDF]
Mol EcolABSTRACT Invasive species that undergo a founder event may experience a decline in genetic diversity yet still establish successful populations. A possible example is a population of the common wall lizard (Podarcis muralis) in Cincinnati, Ohio, USA, which was founded following an introduction in the 1950s of a small number of individuals from Europe ...
Bode ER +5 more
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Fast Outlier Detection Using a Grid-Based Algorithm. [PDF]
PLoS ONE, 2016 As one of data mining techniques, outlier detection aims to discover outlying observations that deviate substantially from the reminder of the data. Recently, the Local Outlier Factor (LOF) algorithm has been successfully applied to outlier detection ...
Jihwan Lee, Nam-Wook Cho
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Energy Reports, 2023 The missing and abnormal data in power transformer operation and monitoring greatly affect the accuracy of fault diagnosis and thus threaten the stable operation of power systems.
Dexu Zou +9 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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Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor
IEEE Access, 2021 As one of the key components of smart grid, advanced metering infrastructure (AMI) provides an immense number of data, making technologies such as data mining more suitable for electricity theft detection.
Yanlin Peng +6 more
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Detection of Outliers in Univariate Circular Data by Means of the Outlier Local Factor (LOF) [PDF]
Statistics in Transition New Series, 2020 Abstract The problem of outlier detection in univariate circular data was the object of increased interest over the last decade. New numerical and graphical methods were developed for samples from different circular probability distributions.
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Robust Incremental Outlier Detection Approach Based on a New Metric in Data Streams
IEEE Access, 2021 Detecting outliers in real time from multivariate streaming data is a vital and challenging research topic in many areas. Recently introduced the incremental Local Outlier Factor (iLOF) approach and its variants have received considerable attention as ...
Ali Degirmenci, Omer Karal
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TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
Sensors, 2020 Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT).
Jen-Wei Huang +2 more
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Clustering-Based Outlier Detection Technique Using PSO-KNN
Journal of Applied Science and Engineering, 2023 In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique.
Sushilata D. Mayanglambam +2 more
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An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
Future Internet, 2022 This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for
Francisco Melo Pereira, Rute C. Sofia
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