Outlier detection algorithm based on k-nearest neighbors-local outlier factor
Journal of Algorithms & Computational Technology, 2022 The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the
He Xu, Lin Zhang, Peng Li, Feng Zhu
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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm [PDF]
Diagnostics, 2022 Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but
Tingting He, Qiaoer Zhou, Yuanwen Zou
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A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution [PDF]
Sensors, 2018 Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by
Shizhen Zhao, Wenfeng Li, Jingjing Cao
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An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm
IEEE Access, 2019 Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra ...
Ping Yang +4 more
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An Efficient Density-Based Local Outlier Detection Approach for Scattered Data
IEEE Access, 2019 After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore
Shubin Su +6 more
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An Incremental Local Outlier Detection Method in the Data Stream
Applied Sciences, 2018 Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao +3 more
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Outlier Detection and Explanation Method Based on FOLOF Algorithm [PDF]
EntropyOutlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to ...
Lei Bai, Jiasheng Wang, Yu Zhou
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Design and analysis of management platform based on financial big data [PDF]
PeerJ Computer Science, 2023 Traditional financial accounting will become limited by new technologies which are unable to meet the market development. In order to make financial big data generate business value and improve the information application level of financial management ...
Yuhua Chen +3 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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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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