Open AccessClustering analysis method of power grid company based on K-means
Author(s) -
Wenting Wang,
Qiang Ma,
Yong Liu,
Ning Yao,
Jing Liu,
Zhaoxuan Wang,
HongLin Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012072
Subject(s) - cluster analysis , computer science , grid , power grid , power (physics) , data mining , identification (biology) , value (mathematics) , distributed computing , artificial intelligence , mathematics , machine learning , physics , geometry , botany , quantum mechanics , biology
With the development of network and the expansion of power grid business, the power grid traffic becomes more and more complex and huge. It is more and more difficult to identify the power grid business through the traffic, and the identification accuracy also needs to be improved. In order to solve this problem, this paper proposes a clustering scheme based on K-means to classify the power grid business through the grid traffic, using elkan K-means algorithm improves the efficiency of the algorithm and finds the most suitable K value for power grid business data.