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• W2148583977 abstract "Research in anomaly detection suffers from a lack of realistic and publicly-available problem sets. This paper discusses what properties such problem sets should possess. It then introduces a methodology for transforming existing classification data sets into ground-truthed benchmark data sets for anomaly detection. The methodology produces data sets that vary along three important dimensions: (a) point difficulty, (b) relative frequency of anomalies, and (c) clusteredness. We apply our generated datasets to benchmark several popular anomaly detection algorithms under a range of different conditions." @default.
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• W2148583977 date "2013-01-01" @default.
• W2148583977 modified "2023-10-17" @default.
• W2148583977 title "Systematic construction of anomaly detection benchmarks from real data" @default.
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• W2148583977 doi "https://doi.org/10.1145/2500853.2500858" @default.
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