Research Article
Data Clustering Mining Method of Social Network Talent Recruitment Stream Based on MST Algorithm
@INPROCEEDINGS{10.1007/978-3-031-28867-8_8, author={Hongjian Li and Nan Hu}, title={Data Clustering Mining Method of Social Network Talent Recruitment Stream Based on MST Algorithm}, proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2023}, month={3}, keywords={MST algorithm Social network Talent recruitment Stream data Clustering Data processing}, doi={10.1007/978-3-031-28867-8_8} }- Hongjian Li
Nan Hu
Year: 2023
Data Clustering Mining Method of Social Network Talent Recruitment Stream Based on MST Algorithm
ADHIP PART 2
Springer
DOI: 10.1007/978-3-031-28867-8_8
Abstract
In order to solve the problem that the data clustering mining method of social network talent recruitment stream is affected by the score of graph area and has a long time of index updating, a data clustering mining method of social network talent recruitment stream based on MST algorithm is designed. Based on the six-degree segmentation theory, the features of social network talent recruitment are extracted, the flow computation framework is established, the recruitment data processing process is optimized, and the similarity coefficient is used as similarity measure to construct the flow data clustering model and the mining pattern is designed by using the MST algorithm. Experimental results show that the maximum update time of the proposed method is 16.638 ms, which shows that the proposed method can shorten the update time of the index and is of high value.
