Parallel Algorithm of Flow Data Anomaly Detection Based on Isolated Forest
2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)(2020)
摘要
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.
更多查看译文
关键词
isolated forest algorithm, anomaly detection, Flink, parallelization
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要