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Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data
IEEE Transactions on Cybernetics, 2021Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data.
Zhong Yuan +4 more
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Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
The Web Conference, 2021Outlier detection is an important task in many domains and is intensively studied in the past decade. Further, how to explain outliers, i.e., outlier interpretation, is more significant, which can provide valuable insights for analysts to better ...
Hongzuo Xu +6 more
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IEEE Transactions on Knowledge and Data Engineering
Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while ...
Xianyong Zhang, Zhong Yuan, Duoqian Miao
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Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while ...
Xianyong Zhang, Zhong Yuan, Duoqian Miao
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2018
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article. Detection of outliers is an important explorative step in empirical analysis.
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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article. Detection of outliers is an important explorative step in empirical analysis.
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Communications in Statistics - Theory and Methods, 1987
In most physical sciences, vast amounts of data are collected which must be edited for erroneous data points (or outliers) before they can be analyzed. The nature of the data is such that standard data-editing procedures are not applicable, for the data typically have a varying mean and covariance structure.
Ranjit M. Passi +2 more
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In most physical sciences, vast amounts of data are collected which must be edited for erroneous data points (or outliers) before they can be analyzed. The nature of the data is such that standard data-editing procedures are not applicable, for the data typically have a varying mean and covariance structure.
Ranjit M. Passi +2 more
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ACM Transactions on Management Information Systems, 2020
Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and ...
Asma Belhadi +3 more
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Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and ...
Asma Belhadi +3 more
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WIREs Computational Statistics, 2009
AbstractWe present an overview of the major developments in the area of detection of outliers. These include projection pursuit approaches as well as Mahalanobis distance‐based procedures. We also discuss principal component‐based methods, since these are most applicable to the large datasets that have become more prevalent in recent years.
Ali S. Hadi +2 more
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AbstractWe present an overview of the major developments in the area of detection of outliers. These include projection pursuit approaches as well as Mahalanobis distance‐based procedures. We also discuss principal component‐based methods, since these are most applicable to the large datasets that have become more prevalent in recent years.
Ali S. Hadi +2 more
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Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010
A spatial outlier is a spatially referenced object whose non-spatial attributes are very different from those of its spatial neighbors. Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed.
Xutong Liu, Chang-Tien Lu, Feng Chen
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A spatial outlier is a spatially referenced object whose non-spatial attributes are very different from those of its spatial neighbors. Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed.
Xutong Liu, Chang-Tien Lu, Feng Chen
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International Scientific Journal of Engineering and Management
To find observations that differ considerably from the bulk of the data points, outlier detection is an essential task in data analysis. To put it more simply, outliers are individual data points that stand out from the rest of the dataset. the Iris dataset, a machine learning benchmark, is used for outlier detection.
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To find observations that differ considerably from the bulk of the data points, outlier detection is an essential task in data analysis. To put it more simply, outliers are individual data points that stand out from the rest of the dataset. the Iris dataset, a machine learning benchmark, is used for outlier detection.
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Neighborhood outlier detection
Expert Systems with Applications, 2010KNN (k nearest neighbor) is widely discussed and applied in pattern recognition and data mining, however, as a similar outlier detection method using local information for mining a new outlier, neighborhood outlier detection, few literatures are reported on.
Yumin Chen, Duoqian Miao, Hongyun Zhang
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