Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 72 for “"Outlier Detection"”.
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Outlier detection for information networks
… recently has there been some work in the area of outlier detection for information network data. Outlier (or anomaly) detection is a very broad field and has been studied in the context of a large number of application domains. Many algorithms have been proposed for outlier detection in …
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New approaches for outlier detection
Outlier detection has relevance in many modern day contexts, including health care, engineering, data processing and analysis, credit card fraud, monitoring computer and internet intrusions and wearable personal health sensors. Outlier detection once represented a single pre-processing step, …
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Generative Models Driven Graph Outlier Detection
… networks, and communication systems. The detection of outliers in graph data—substructures that significantly deviate from the norm—is crucial for uncovering fraudulent activities, network vulnerabilities, and novel patterns. However, traditional outlier detection techniques often fall …
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Scalable Techniques for Trajectory Outlier Detection
… of data analysis operations such as trajectory outlier detection, which consists in the identification of those trajectories that behave much differently from the rest of the trajectories in a database. There are several time-critical applications such as traffic management systems, security …
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Relational Outlier Detection: Techniques and Applications
Nowadays, outlier detection has attracted growing interest. Unlike typical outlier detection problems, relational outlier detection focuses on detecting abnormal patterns in datasets that contain relational implications within each data point. Furthermore, different from the traditional outlier …
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Robust model selection and outlier detection in linear regressions
… study the problems of robust model selection and outlier detection in linear regression. The results of data analysis based on linear regressions are highly sensitive to model choice and the existence of outliers in the data. This thesis aims to help researchers to choose the correct model when …
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Information-based projection method for categorical clustering and outlier detection.
… projection method for categorical clustering and outlier detection.
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A variance shilf model for outlier detection and estimation in linear and linear mixed models
Outliers are data observations that fall outside the usual conditional ranges of the response data.They are common in experimental research data, for example, due to transcription errors or faulty experimental equipment. Often outliers are quickly identified and addressed, that is, corrected, …
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A variance shift model for outlier detection and estimation in linear and linear mixed models
Outliers are data observations that fall outside the usual conditional ranges of the response data.They are common in experimental research data, for example, due to transcription errors or faulty experimental equipment. Often outliers are quickly identified and addressed, that is, corrected, …
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Abnormal Pattern Recognition in Spatial Data
… mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help …
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Exploring anomaly detection methods using features extracted with a neural network
Anomaly (or outlier) detection is a problem with the goal of detecting outliers—or anomalous samples—within a dataset or distribution. Anomaly detection has important applications in computer vision, cybersecurity, biomedical imaging, and more. As the data becomes bigger (more samples collected) …
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Efficient Algorithms for Mining Data Streams
… of the local region based KDE to multi-scale outlier detection. Theoretical development includes the formulation of the local region concept to effectively approximate the computationally intensive adaptive KDE. This work also analyzes key theoretical properties of the local region based …
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Efficient Algorithms for Mining Large Spatio-Temporal Data
… />three main mining tasks, including spatial outlier detection, robust<br />spatio-temporal prediction, and novel applications to real world<br />problems.<br /><br />Spatial novelty patterns, or spatial outliers, are those data points<br />whose characteristics are markedly different from …
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Time series data analytics : clustering-based anomaly detection techniques for quality control in semiconductor manufacturing
… at ADI to implement such improvements. Anomaly detection techniques can be effective in improving the quality control on semiconductor production lines. Sets of data collected from semiconductor manufacturing machines, such as a plasma etcher, can be analyzed to control the fabrication process …
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Prediction and Anomaly Detection Techniques for Spatial Data
… for three main mining tasks, including spatial outlier detection, robust inference for spatial dataset, and spatial prediction for large multivariate non-Gaussian data. spatial outlier analysis, which aims at detecting abnormal objects in spatial contexts, can help extract important knowledge in …
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A Machine Learning Framework for Securing Patient Records
This research concerns the detection of abnormal data usage and unauthorised access in large-scale critical networks, specifically healthcare infrastructures. The focus of this research is safeguarding Electronic Patient Record (EPR)systems in particular. Privacy is a primary concern amongst …
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Some Advances in Statistical modeling of Brain Structural Connectomes
… no consideration of the important problem of outlier detection in the structural connectomics literature. In particular, for certain subjects, the neuroimaging data are so poor quality that the network cannot be reliably reconstructed. For such subjects, the resulting adjacency matrix may be …
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Examining agricultural investment
… a Bayesian framework with variable selection and outlier detection components. The results imply strong support for the accelerator model of investment and the inclusion of other relevant variables, among them the value of short-term assets, one of the proxies for the 5 Cs. Another of the proxies, …
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