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A Novel Outlier Detection Method for Multivariate Data
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Outgoing claims: the papers this work cites, builds on, or argues with.
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High-Dimensional Outlier Detection: The Subspace Method
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Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
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A precise ranking method for outlier detection
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Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
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A comparative evaluation of outlier detection algorithms: Experiments and analyses
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A Survey of Outlier Detection Methodologies
inferredcites →https://doi.org/10.1073/pnas.97.4.1423openalex-snapshot
The multivariate L 1 -median and associated data depth
inferredcites →https://doi.org/10.1109/icdm.2012.112openalex-snapshot
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
inferredcites →https://doi.org/10.1109/icdm.2012.21openalex-snapshot
Outlier Detection in Arbitrarily Oriented Subspaces
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Outlier Detection with Autoencoder Ensembles
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Subspace clustering for high dimensional data
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Feature bagging for outlier detection
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Angle-based outlier detection in high-dimensional data
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A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data
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Efficient algorithms for mining outliers from large data sets
inferredcites →https://doi.org/10.12988/ams.2015.53213openalex-snapshot
Outlier detection in multivariate data
inferredcites →https://doi.org/10.4135/9781412973380.n22openalex-snapshot
An Introduction to Data Mining
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Deep Learning for Anomaly Detection: A Survey
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SURVEY PAPER ON OUT LIER DETECTION USING FUZZY LOGIC BASED METHOD
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