Research on Multidimensional Power Big Data Clustering Algorithm Based on Graph Mode

• Published in: Journal of Advanced Computational Intelligence and Intelligent Informatics Vol. 29; no. 2; pp. 358 - 364
• Main Authors: Han, Xue, Zhang, Yue, Gao, Sheng
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Ltd 20.03.2025; 富士技術出版株式会社; Fuji Technology Press Co. Ltd
• Subjects: Accuracy; Algorithms; Big Data; Clustering; clustering algorithm; Datasets; Delay time; Eigenvalues; Eigenvectors; Electric power; Electricity distribution; Energy consumption; fisher discriminant; graph model; multidimension; Parallel processing; power big data; Principal components analysis; Redundancy
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2025.p0358

Power system data possess many characteristics and indicators, having certain high dimensions and redundant information, which can easily increase the calculation and storage overhead. To reduce the dimension of power data, eliminate redundant information, and reduce the delay time, a data clustering algorithm is proposed. Firstly, an algorithm based on PCA and kernel local Fisher identification is used to reduce the dimension of large multidimensional samples and enhance the accuracy of subsequent clustering. Thereafter, the redundant data are processed after dimension reduction is processed to optimize the data quality by introducing a bloom filter structure. In the graph model, data clustering is completed based on the parallel processing of redundant data. Simulation results show that the correctness and stability of this method are over 85%, and the delay time is decreased, representing good application prospects.