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A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams [PDF]
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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Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2019 Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a ...
Silvano Sugidamayatno, Danang Lelono
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Local outlier factor algorithm based on correction of bidirectional neighbor
Tongxin xuebao, 2020 A local outlier factor algorithm based on bidirectional neighbor correction was proposed to solve the problems of existing outlier detection algorithms such as difficulty in parameter selection,poor efficiency and low accuracy.The bidirectional neighbor ...
Xiaohui YANG, Xiaoming LIU
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Jurnal Lebesgue, 2023 Indonesia's location on the "Ring of Fire" poses a high risk for seismic events. Addressing this, our study applied the Local Outlier Factor (LOF) algorithm for advanced seismic anomaly detection, crucial for geotectonic upheaval prediction.
Gregorius Airlangga
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IEEE AccessIn today’s interconnected world, cybersecurity has emerged as a critical domain for ensuring the integrity, confidentiality, and availability of digital assets.
Taher Ali Al-Shehari +6 more
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Measuring Novelty in Autonomous Vehicles Motion Using Local Outlier Factor Algorithm
CoRR, 2021 Under unexpected conditions or scenarios, autonomous vehicles (AV) are more likely to follow abnormal unplanned actions, due to the limited set of rules or amount of experience they possess at that time. Enabling AV to measure the degree at which their movements are novel in real-time may help to decrease any possible negative consequences.
Hassan Alsawadi, Muhammad Bilal
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A new outlier detection algorithm based on observation-point mechanism
Shenzhen Daxue xuebao. Ligong ban, 2022 Outlier detection is an important branch of data mining research, and has wide applications in the fields of finance, telecommunications, and biology. The traditional nearest neighbor-based outlier detection (NNOD) and local outlier factor-based outlier ...
YU Wanguo, HE Yulin, QIN Huilin
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Unsupervised Outlier Detection Mechanism for Tea Traceability Data
IEEE Access, 2022 The presence of outliers in tea traceability data can mislead customers and have a significant impact on the reputation and profits of tea companies. To solve this problem, an unsupervised outlier detection mechanism for tea traceability data is proposed.
Honggang Yang +4 more
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Entropy, 2023 Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data ...
Zihao Li, Liumei Zhang
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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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