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Outliers in Microarray Data Analysis
2006This paper presents a broad survey of analysis methods that have been found useful in the detection and treatment of outliers, or “anomalous data points,” that often arise in large datasets. One source of these data anomalies is method outliers like “gross measurement errors” that are of no inherent biological interest, but a second source is ...
Ronald K. Pearson +2 more
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Outlier Detection in Cointegration Analysis
Journal of Business & Economic Statistics, 1998Franses, Philip Hans, Lucas, A (André)
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An Introduction to Outlier Analysis
2012Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data mining and statistics literature. In most applications, the data is created by one or more generating processes, which could either reflect activity in the system or observations collected about entities.
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Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data
IEEE Transactions on Cybernetics, 2022Zhong Yuan, Hongmei Chen, Trli30
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A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams
Big Data and Cognitive Computing, 2021Omar Alghushairy +2 more
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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Angel Conde, Jose A A Lozano
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