Multi attribute data classification mining method for power grid cloud platform

In order to achieve efficient processing of complex multidimensional data generated by power grid operation and improve the intelligence level of power grid operation, this paper studies the multi-attribute data classification mining method in the power grid cloud platform. This method uses decision tree optimization and feature selection techniques to extract key attribute features from power grid data. Based on the C5.0 decision tree algorithm, it accurately identifies and classifies key attributes in power grid data, thereby achieving real-time monitoring, anomaly detection, and optimized scheduling of power grid status. The experimental results show that this method has high accuracy and good classification efficiency in mining multi-attribute data on the power grid cloud platform, which can improve the stability and security of power grid operation and provide strong data support for the refined management and decision-making of smart grids.