Results 271 to 280 of about 4,573,641 (310)
Some of the next articles are maybe not open access.
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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Robust Regression and Outlier Detection
Wiley Series in Probability and Statistics, 2005P. Rousseeuw, A. Leroy
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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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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Angel Conde, Jose A A Lozano
exaly
A survey of outlier detection in high dimensional data streams
Computer Science Review, 2022Imen Souiden +2 more
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2012
The discussions in the previous chapters focus on the problem of unsupervised outlier detection in which no prior information is available about the abnormalities in the data. In such scenarios, many of the anomalies found correspond to noise or other uninteresting phenomena.
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The discussions in the previous chapters focus on the problem of unsupervised outlier detection in which no prior information is available about the abnormalities in the data. In such scenarios, many of the anomalies found correspond to noise or other uninteresting phenomena.
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Graph autoencoder-based unsupervised outlier detection
Information Sciences, 2022Xusheng Du +4 more
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