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ACM Computing Surveys, 2020
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
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Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
openaire +1 more source
Enhancing Outlier Detection by an Outlier Indicator
2018Outlier detection is an important task in data mining and has high practical value in numerous applications such as astronomical observation, text detection, fraud detection and so on. At present, a large number of popular outlier detection algorithms are available, including distribution-based, distance-based, density-based, and clustering-based ...
Xiaqiong Li, Xiaochun Wang, Xia Li Wang
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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
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AN ALGORITHM OF DETECTING OUTLIERS IN SVR
International Journal of Wavelets, Multiresolution and Information Processing, 2012This paper defines an outlier and a measure that an outlier does not fit the theoretical model in the regression problems, studies the relationship between the theoretical model and the regression model in the regression problems, proposes and verifies an approximate theorem in which one-by-one deletes outlier and constructs SVR to approximate its ...
Shaohua Zeng +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
openaire +1 more source
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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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.
+5 more sources
A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022U Mori +2 more
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