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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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Batch Process Modeling and Monitoring With Local Outlier Factor
IEEE Transactions on Control Systems Technology, 2019Batch processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, batch-to-batch processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for ...
Jinlin Zhu +3 more
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An Efficient Switching Median Filter Based on Local Outlier Factor
IEEE Signal Processing Letters, 2011An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND).
Wei Wang, Peizhong Lu
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Local Outlier Factor Based False Data Detection in Power Systems
2019 IEEE Sustainable Power and Energy Conference (iSPEC), 2019The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems,
Yifan Ou, Bin Deng, Xuan Liu, Ke Zhou
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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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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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Detecting DDoS Attack in Cloud Computing Using Local Outlier Factors
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018Now a days, Cloud computing has brought a unbelievable change in companies, organizations, firm and institutions etc. IT industries is advantage with low investment in infrastructure and maintenance with the growth of cloud computing. The Virtualization technique is examine as the big thing in cloud computing.
G. Madhupriya +2 more
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Anomalous cell detection with kernel density-based local outlier factor
China Communications, 2015Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we
Dandan Miao, Xiaowei Qin, Weidong Wang
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Universal blind steganalysis via reference points-based local outlier factor
2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), 2017We consider a particular paradigm of steganalysis, called universal blind steganalysis, namely, no knowledge of the steganographic way and all knowledge of the cover-source. Its goal is to detect all known (already existing) and unknown (previously unseen) steganographic algorithms in such a paradigm.
Xiaodan Hou, Tao Zhang
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Journal of Chemometrics, 2018
AbstractIndustrial processes typically have multiple operating modes with complex data distribution and locality faults, which challenges the traditional multivariate statistical process monitoring methods. To address this problem, a double‐level local information‐based local outlier factor (LOF) method is proposed in this work for multimode complex ...
Lei Wang, Xiaogang Deng, Yuping Cao
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AbstractIndustrial processes typically have multiple operating modes with complex data distribution and locality faults, which challenges the traditional multivariate statistical process monitoring methods. To address this problem, a double‐level local information‐based local outlier factor (LOF) method is proposed in this work for multimode complex ...
Lei Wang, Xiaogang Deng, Yuping Cao
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