A critical overview of outlier detection methods
A Smiti - Computer Science Review, 2020 - Elsevier
One of the opening steps towards obtaining a reasoned analysis is the detection of outlaying
observations. Even if outliers are often considered as a miscalculation or noise, they may …
observations. Even if outliers are often considered as a miscalculation or noise, they may …
The research progress and prospect of data mining methods on corrosion prediction of oil and gas pipelines
L Xu, Y Wang, L Mo, Y Tang, F Wang, C Li - Engineering Failure Analysis, 2023 - Elsevier
As the principal means of oil and natural gas transportation, oil and gas pipeline systems
suffer from common corrosion problems, accurate corrosion prediction of oil and gas …
suffer from common corrosion problems, accurate corrosion prediction of oil and gas …
An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine
Realization of the importance for advanced tool and techniques to secure the network
infrastructure from the security risks has led to the development of many machine learning …
infrastructure from the security risks has led to the development of many machine learning …
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
A novel outlier detection approach based on formal concept analysis
Q Hu, Z Yuan, K Qin, J Zhang - Knowledge-Based Systems, 2023 - Elsevier
Outlier detection is a major research field for data mining. In recent years, rough set and
granular computing have been successfully applied to outlier detection, and a series of …
granular computing have been successfully applied to outlier detection, and a series of …
Fuzzy granular anomaly detection using Markov random walk
Fuzzy information granulation is an important mathematical model in the theory of granular
computing that can effectively handle fuzzy or uncertain information. To address the …
computing that can effectively handle fuzzy or uncertain information. To address the …
Anomaly detection with representative neighbors
Identifying anomalies from data has attracted increasing attention in recent years due to its
broad range of potential applications. Although many efforts have been made for anomaly …
broad range of potential applications. Although many efforts have been made for anomaly …
An outlier detection algorithm based on cross-correlation analysis for time series dataset
Outlier detection is a very essential problem in a variety of application areas. Many detection
methods are deficient for high-dimensional time series data sets containing both isolated …
methods are deficient for high-dimensional time series data sets containing both isolated …
Fusing multi-scale fuzzy information to detect outliers
Outlier detection aims to find objects that behave differently from the majority of the data.
Existing unsupervised approaches often process data with a single scale, which may not …
Existing unsupervised approaches often process data with a single scale, which may not …
Attribute-weighted outlier detection for mixed data based on parallel mutual information
J Li, Z Liu - Expert Systems with Applications, 2024 - Elsevier
Outlier detection plays an important role in data mining because it can improve the
performance of data analysis. Most outlier detection algorithms focus on numerical or …
performance of data analysis. Most outlier detection algorithms focus on numerical or …