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A Comparative Study of Local Outlier Factor Algorithms for Outliers Detection in Data Streams
2018Outlier detection analyzes data, finds out anomalies, and helps to discover unforeseen activities in safety crucial systems. Outlier detection helps in early prediction of various fraudulent activities like credit card theft, fake insurance claim, tax stealing, real-time monitoring, medical systems, online transactions, and many more.
Supriya Mishra, Meenu Chawla
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Distributed Local Outlier Factor with Locality-Sensitive Hashing
2019Outlier detection remains a heated area due to its essential role in a wide range of applications, including intrusion detection, fraud detection in finance, medical diagnosis, etc. Local Outlier Factor (LOF) has been one of the most influential outlier detection techniques over the past decades.
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2019 6th International Conference on Systems and Informatics (ICSAI), 2019
Detecting the outliers for transformer's oil chromatographic data is very important for analyzing and monitoring the status of a transformer. However, the existing methods are usually developed for some specific situations and may not perform well in many cases.
Jiafeng Qin +4 more
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Detecting the outliers for transformer's oil chromatographic data is very important for analyzing and monitoring the status of a transformer. However, the existing methods are usually developed for some specific situations and may not perform well in many cases.
Jiafeng Qin +4 more
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Self-Adaptive Negative Selection Using Local Outlier Factor
2012Negative selection algorithm (NSA) classifies a given data either as normal (self) or anomalous (non-self). To make this classification, it is trained using normal (self) samples. NSA generates detectors to cover the complementary space of self in training phase. The classification of NSAs is mainly specified by two issues, self space determination and
Zafer Ataser, Ferda Nur Alpaslan
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Distribution Adaptation Local Outlier Factor for Multimode Process monitoring
2020 39th Chinese Control Conference (CCC), 2020In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex.
Yutang Xiao, Yang Tao, Hongbo Shi
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Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, 2010The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned an LOF which represents the likelihood of
Malak Alshawabkeh +2 more
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Filtered Clustering Based on Local Outlier Factor in Data Mining
International Journal of Database Theory and Application, 2016In this paper, the impact of -means and local outliner factor on data set is studied. Outlier is the observation which is different from or inconsistent with the rest of the data. However, the main challenges of outlier detection are increasing complexity due to variety of datasets and size of dataset.
Vishal Bhatt +2 more
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Combination of Local Outlier Factor and Winsorization for Clustering Outlier in Medical Records
2023 11th International Conference on Information and Communication Technology (ICoICT), 2023Gohan Bonar Pinio Sinaga +2 more
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A Novel Noise Clustering Based on Local Outlier Factor
2023Yukihiro Hamasuna, Yoshitomo Mori
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Derivative-free optimization (DFO) is a method that does not require the calculation of gradients or higher-order derivatives of the objective function, making it suitable for cases where the objective function is non-differentiable or the computation of derivatives is expensive.
Zhang, Qi, Xie, Pengcheng
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Zhang, Qi, Xie, Pengcheng
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