Research Article
Research on Difference Elimination Method Between Small Sample Databases Based on Feature Extraction
@INPROCEEDINGS{10.1007/978-3-030-94185-7_16, author={Jin-hua Liu and Fu-lian Zhong}, title={Research on Difference Elimination Method Between Small Sample Databases Based on Feature Extraction}, proceedings={IoT and Big Data Technologies for Health Care. Second EAI International Conference, IoTCare 2021, Virtual Event, October 18-19, 2021, Proceedings, Part I}, proceedings_a={IOTCARE}, year={2022}, month={6}, keywords={Small sample database Data clustering Data fusion Difference elimination}, doi={10.1007/978-3-030-94185-7_16} }- Jin-hua Liu
Fu-lian Zhong
Year: 2022
Research on Difference Elimination Method Between Small Sample Databases Based on Feature Extraction
IOTCARE
Springer
DOI: 10.1007/978-3-030-94185-7_16
Abstract
The traditional method of eliminating the differences between small sample databases takes a long time and has a low accuracy. Therefore, a method of eliminating the differences between small sample databases based on feature extraction is designed. In order to realize the data communication between small sample databases, we construct the data retention mechanism of small sample databases, store the sample data safely, discretize the data attributes, sort the primary and secondary relationship of the sample data, select the optimal integration and sharing path of the sample data, cluster the sample data, and select the cluster head and relay node, Eliminate the differences between small sample databases. The experimental results show that compared with the traditional method, this design method shortens the time of eliminating the differences between small sample databases, and improves the accuracy of eliminating the differences.
