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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.
openaire +1 more source
Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data
IEEE Transactions on Cybernetics, 2022Zhong Yuan, Hongmei Chen, Trli30
exaly
A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams
Big Data and Cognitive Computing, 2021Omar Alghushairy +2 more
exaly
Multivariate outlier detection
1980The concepts of parametric outlier testing extend with some difficulty but only relatively minor modification to multivariate data. Suppose that X 1, X 2, . . . , X n are n vectors ofp components, the null hypothesis being that they are a random sample from the multivariate normal distribution with mean vector ξ and covariance matrix ∑ $$ H_0 ...
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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Angel Conde, Jose A A Lozano
exaly