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

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 clusterin...

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Published inJournal 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Han Xue
Zhang Yue
Gao Sheng
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  surname: Gao
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  organization: SICT Shenyang Institute of Computing Technology Co. Ltd., CAS, Shenyang, Liaoning 110000, China
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SubjectTerms 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
Title Research on Multidimensional Power Big Data Clustering Algorithm Based on Graph Mode
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Volume 29
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