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- W3094186747 abstract "The isolated forest algorithm is improved and applied to the hydrological field. The parallel anomaly detection algorithm (Flink-iForest) is proposed. At the same time, the k-means algorithm is combined to solve the problem of Flink-iForest threshold division and improve the stability of anomaly detection results. Through various experiments and real hydrological data, first of all, the Flink-iForest algorithm is verified in terms of accuracy, efficiency and scalability, and compared with the standard SKlearn-iForest and PIFH algorithm; finally, the effectiveness and efficiency of Flink-iForest algorithm are proved by experiments." @default.
- W3094186747 created "2020-10-29" @default.
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- W3094186747 date "2020-06-01" @default.
- W3094186747 modified "2023-09-26" @default.
- W3094186747 title "Parallel Algorithm of Flow Data Anomaly Detection Based on Isolated Forest" @default.
- W3094186747 cites W2963944930 @default.
- W3094186747 doi "https://doi.org/10.1109/aiea51086.2020.00035" @default.
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