Research Article
Clustering Mining Method of College Students’ Employment Data Based on Feature Selection
@INPROCEEDINGS{10.1007/978-3-030-94551-0_9, author={Mei-bin Qi}, title={Clustering Mining Method of College Students’ Employment Data Based on Feature Selection}, proceedings={Advanced Hybrid Information Processing. 5th EAI International Conference, ADHIP 2021, Virtual Event, October 22-24, 2021, Proceedings, Part I}, proceedings_a={ADHIP}, year={2022}, month={1}, keywords={College student Employment data Clustering Excavate Feature selection}, doi={10.1007/978-3-030-94551-0_9} }- Mei-bin Qi
Year: 2022
Clustering Mining Method of College Students’ Employment Data Based on Feature Selection
ADHIP
Springer
DOI: 10.1007/978-3-030-94551-0_9
Abstract
In order to further clarify the employment trend and employment status of college students, this paper proposes a clustering mining method for college students’ employment data based on feature selection. This paper analyzes the employment market of college students, and gives the data structure of college Students’ employment. On this basis, the employment data of college students are normalized and compressed. Based on the preprocessed employment data of college students, the sparse score method is used to select data features. And through online clustering strap algorithm deep Clustering Mining College Students’ employment data, so as to realize the clustering mining of College Students’ employment data. The experimental data show that the clustering accuracy of the existing methods ranges from 45.23% to 54.79%, and the clustering accuracy of the proposed methods ranges from 70.15% to 83.54%. Through data comparison, it is found that compared with the existing methods, the clustering accuracy of the proposed method is higher, which fully shows that the clustering mining effect of the proposed method is better.
