In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unaut...
Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for rel...
Detecting anomalies in engine and machinery data during ship operations is crucial for maintaining the safety and efficiency of the vessel. We conducted experiments using device data from the maritime industry, consisting of time series records from...
The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling pl...
In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usu...
The k-nearest neighbor (kNN) method only uses samples’ paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set c...
Air pollution affects human health and is one of the main problems in the world, including in coastal cities with industrial seaports. In this sense, the city of Gijón (northern Spain) stands out as one of the 20 Spanish cities with the worst...
The present research uses two different functional data analysis methods called functional high-density region (HDR) boxplot and functional bagplot. Both methodologies were applied for the outlier detection in the time pollutant emissions curves that...
Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is...
Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led...
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. The pe...
Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues...
We consider functional outlier detection from a geometric perspective, specifically: for functional datasets drawn from a functional manifold, which is defined by the data’s modes of variation in shape, translation, and phase. Based on this man...
Outlier 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...
Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group fo...
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. Recent maj...
Community detection in social networks is of great importance and is used in a variety of applications such as recommendation systems and targeted advertising. While detecting dense groups with high levels of connectivity and similar interests betwee...
In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches ha...
A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised metho...
Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detec...
The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these...
Outliers often occur during data collection, which could impact the result seriously and lead to a large inference error; therefore, it is important to detect outliers before data analysis. Gamma distribution is a popular distribution in statistics;...
Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are...
Data clustering is a fundamental machine learning task found in many real-world applications. However, real data usually contain noise or outliers. Handling outliers in a clustering algorithm can improve the clustering accuracy. In this paper, we pro...
In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points...
Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive con...
Outlier detection plays a key role in data analysis by improving data quality, uncovering data entry errors, and spotting unusual patterns, such as fraudulent activities. Choosing the right detection method is essential, as some approaches may be too...
Outlier detection is pivotal in data mining and machine learning, as it focuses on discovering unusual behaviors that deviate substantially from the majority of data samples. Conventional approaches, however, often falter when dealing with complex da...
The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are avai...
Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter sel...
Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data...
An outlier, known as an error state, can bring valuable cognitive analytic results in many industrial applications. Aiming at detecting outliers as soon as they appear in data streams that continuously arrive from data sources, this paper presents an...
Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorit...
Noisy data and outliers are among the main challenges in machine learning, as their presence in training data can significantly reduce model performance and generalization. Detecting and handling these anomalous samples is particularly difficult beca...
Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainab...
Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlie...
Outlier detection is a critical task in time series analysis, essential to maintaining data quality and allowing for accurate subsequent analysis. The Hampel filter, a decision filter that replaces outliers in a data window with the median, is widely...
Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal...
In ocean bottom node (OBN) seismic exploration, the precise positioning of OBNs directly affects seismic data quality. However, complex marine environments often introduce intricate outliers into collected acoustic positioning data, which severely re...
Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportuni...
Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlie...
Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised...
The reliability of geomechanical models and engineering designs depend heavily on high-quality data. In geomechanical projects, collecting and analyzing laboratory data is crucial in characterizing the mechanical properties of soils and rocks. Howeve...
Outlier detection is a critical task in the intelligent operation and maintenance (O&M) of transportation equipment, as it helps ensure the safety and reliability of systems like high-speed trains, aircraft, and intelligent vehicles. Nearest neig...
The identification of anomalous data objects within massive datasets is a critical technique in financial auditing. Most existing methods, however, focus on global outlier anomalies detection with less effective in contexts such as Chinese financial...
Fairness in peer review is of vital importance in academic activities. Current peer review systems focus on matching suitable experts with proposals but often ignore the existence of outliers. Previous research has shown that outlier scores in review...
This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtai...
The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is gro...
This study introduces a novel algorithm tailored for the precise detection of lower outliers (i.e., data points at the lower tail) in univariate datasets, which is particularly suited for scenarios with a single cluster and similar data distribution....
In this paper we propose an outlier detection approach for GNSS vector networks based on the specific direction (i.e., SD approach), along which the test statistic constructed reaches the maximum. We derive the unit vector of this specific direction...
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