Anomaly data mining method for electric power data center business system based on decision tree

Bin Li; Shuang Wu; Ye Yu; Zhongying Zhao; Siyao Liu; Peng Li

doi:10.1117/12.3029003

22 May 2024 Anomaly data mining method for electric power data center business system based on decision tree

Bin Li, Shuang Wu, Ye Yu, Zhongying Zhao, Siyao Liu, Peng Li

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762D (2024) https://doi.org/10.1117/12.3029003
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Most of the current frameworks for outlier data mining are one-way structures, and the efficiency of data mining is low, which leads to the increase of data mining redundancy. Therefore, this paper proposes the design and analysis of outlier data mining methods for power data middle office business systems based on decision trees. According to the actual mining requirements and standards, the initial outlier data sources are collected, and the multi-level method is adopted to improve the efficiency of data mining, and the multi-level power data system data mining framework is designed. On this basis, the decision tree outlier data mining model is constructed, and the unit grid integration is used to realize outlier data mining. The final test results show that: for the selected five data mining test cycles, the final data mining redundancy is better held 20%, which indicates that the comprehensive decision tree, the designed G power data middle office business system outlier data mining effect has been significantly improved, targeted, mining processing effect is better, and has practical application value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Li, Shuang Wu, Ye Yu, Zhongying Zhao, Siyao Liu, and Peng Li "Anomaly data mining method for electric power data center business system based on decision tree", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762D (22 May 2024); https://doi.org/10.1117/12.3029003

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