Issue |
E3S Web Conf.
Volume 256, 2021
2021 International Conference on Power System and Energy Internet (PoSEI2021)
|
|
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Article Number | 02040 | |
Number of page(s) | 5 | |
Section | Energy Internet R&D and Smart Energy Application | |
DOI | https://doi.org/10.1051/e3sconf/202125602040 | |
Published online | 10 May 2021 |
Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules
1 Big Data Center of State Grid Corporation of China, Beijing, 100052, China
2 AnHui Jiyuan Software Co., Ltd, Hefei, Anhui, 230088, China
3 Beijing Sgitg Accenture Information Technology Center Co., Ltd, Beijing, 100052 China
* E-mail: dongxinwei@sgitg.sgcc.com.cn
The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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