Results 21 to 30 of about 89,907 (213)
A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method
Information, 2019 Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information.
Liying Wang +5 more
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Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps
Big Data and Cognitive Computing, 2020 Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection.
Menglu Li, Rasha Kashef, Ahmed Ibrahim
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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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International Journal of Distributed Sensor Networks, 2015 Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge.
Haiqing Yao, Heng Cao, Jin Li
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BMC Medical Research Methodology, 2023 Background Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers.
Paraskevi Massara +9 more
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Advancements of Outlier Detection: A Survey
EAI Endorsed Transactions on Scalable Information Systems, 2013 Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements
Ji Zhang
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Speedup Two-Class Supervised Outlier Detection
IEEE Access, 2018 Outlier detection is an important topic in the community of data mining and machine learning. In two-class supervised outlier detection, it needs to solve a large quadratic programming whose size is twice the number of samples in the training set.
Yugen Yi +3 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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Conserved binding mode but diverse interfaces of MreC‐PBP2 interactions
FEBS Letters, EarlyView.The crystal structure of abMreC reveals a conserved two β‐barrel architecture and provides structural insights into its role within the bacterial elongasome. The abMreC–abPBP2 complex model identifies the molecular basis of MreC‐mediated PBP2 recognition, contributing to the regulation of peptidoglycan synthesis.
Hyunseok Jang +4 more
wiley +1 more source
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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