Advanced deep graph neural networks for explainable anomaly detection study

点击翻译按钮获取中文摘要

In this fiscal year, there are totally three achievements achieved, including granular data description, granular ADD algorithms and its application.The first achievement is about granular data description, we have developed advanced granular computing for data description, aiming to reduce the computation cost of big data analysis. Especially, structural granules are constructed for data distribution description and data structure mining in this task.The second one is about anomaly data detection algorithms. By making use of the constructed granules, especially ones having the ability of describing data structure, a primary anomaly data detection method via rule-based modeling method was built. Its performance was evaluated good on both supervised scenarios with noise and unsupervised scenarios.The third achievement is about completing some industrial applications related to anomaly data detection. Through these applications, it is verified useful by considering data structure information in anomaly data detection.