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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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2009
Knowledge discovery in databases (KDD) is a nontrivial process of detecting valid, novel, potentially useful and ultimately understandable patterns in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). In general KDD tasks can be classified into four categories i) Dependency detection, ii) Class identification, iii) Class description and ...
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Knowledge discovery in databases (KDD) is a nontrivial process of detecting valid, novel, potentially useful and ultimately understandable patterns in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). In general KDD tasks can be classified into four categories i) Dependency detection, ii) Class identification, iii) Class description and ...
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2003
The problem of outlier detection has been studied in the context of several domains and has received attention from the database research community. To the best of our knowledge, work up to date focuses exclusively on the problem as follows [10]: “given a single set of observations in some space, find those that deviate so as to arouse suspicion that ...
Spiros Papadimitriou, Christos Faloutsos
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The problem of outlier detection has been studied in the context of several domains and has received attention from the database research community. To the best of our knowledge, work up to date focuses exclusively on the problem as follows [10]: “given a single set of observations in some space, find those that deviate so as to arouse suspicion that ...
Spiros Papadimitriou, Christos Faloutsos
openaire +1 more source