Research Article
Research on Hierarchical Mining Algorithm of Spatial Big Data Set Association Rules
@INPROCEEDINGS{10.1007/978-3-030-36405-2_21, author={Yue Wang and Wei Song}, title={Research on Hierarchical Mining Algorithm of Spatial Big Data Set Association Rules}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2019}, month={11}, keywords={Cloud storage Database Spatial big data Association rules Hierarchical mining}, doi={10.1007/978-3-030-36405-2_21} }- Yue Wang
Wei Song
Year: 2019
Research on Hierarchical Mining Algorithm of Spatial Big Data Set Association Rules
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-36405-2_21
Abstract
Aiming to improve the security of large database in cloud storage space, a hierarchical mining algorithm of spatial big data set association rules based on association dimension feature detection is proposed. The statistical characteristic quantity of large spatial data set is constructed by means of group sample regression analysis, and the sampling and sample recognition of spatial big data set are carried out by using fuzzy rough set mapping method. The association rule distribution model of large spatial datasets is constructed by using the hierarchical mining method of association rules, and the feature quantities of association rules are extracted from large spatial datasets. The correlation dimension feature extraction algorithm is used to optimize the extraction process of large spatial data sets adaptively, so as to realize the hierarchical mining optimization of spatial big data set association rules. The simulation results show that the proposed method has higher accuracy, higher mining accuracy and better feature matching ability, which improves the mining ability of association rules in large database in cloud storage space.
