Dimension Reduction Algorithm of Power and Government-Enterprise Data Fusion Based on Principal Component Analysis

Aiming at the high-dimensional characteristics of electric power and government-enterprise data, this paper proposes a data fusion dimension reduction algorithm based on principal component analysis (PCA). This method aims to extract the main components from the data by PCA technology, so as to reduce the dimension of the data, while retaining key information, which is convenient for subsequent data processing and analysis. On this basis, K-Means clustering algorithm is further applied to cluster the data after dimensionality reduction to identify different electricity consumption behaviors or enterprise types. The experimental results show that the algorithm can effectively reduce the dimension of electric power and government-enterprise data, and reveal the potential structure and pattern in the data. The distribution of data points in two-dimensional space is observed by clustering, which provides powerful data support and decision-making basis for power enterprises and government agencies. This study not only improves the efficiency of data processing, but also provides new ideas and methods for data mining and analysis in related fields.