A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.
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Cites background from "A Comparative Evaluation of Unsuper..."
...alizability, or computational expense [9, 16] (see [9] for a survey of anomaly detection approaches)....
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...Utility across application domains, data types, and anomaly types has ensured that a wide variety of anomaly detection approaches have been studied [9, 16]....
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"A Comparative Evaluation of Unsuper..." refers background or methods in this paper
...The task of detecting single anomalous instances in a larger dataset (as introduced so far) is called point anomaly detection [15]....
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...One-class support vector machines [24] are often used for semi-supervised anomaly detection [15]....
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...Unsupervised anomaly detection algorithms can be roughly categorized into the following main groups [15] as illustrated in Fig 3: (1) Nearest-neighbor based techniques, (2) Clusteringbased methods and (3) Statistical algorithms....
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"A Comparative Evaluation of Unsuper..." refers background in this paper
...5 [20] cannot deal well with unbalanced data, whereas Support Vector Machines (SVM) [21] or Artificial Neural Networks (ANN) [22] should perform better....
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