Title: Research on the application of association rules based on information entropy in human resource management

Authors: Yi Wang; Lei Li

Addresses: Department of Accounting, Shijiazhuang Post and Telecommunication Technical College, 050021, China ' Junior High School Department, Shijiazhuang Foreign Language Education Group, 050021, China

Abstract: The informatisation process of human resource management requires the face of massive data, and association rule algorithms can efficiently mine the relationships between itemsets from massive data. The Apriori algorithm is widely used due to its advantages such as simple operation, but it is prone to generating a large number of candidate itemsets and fails to consider the differences in the importance of different attributes. To solve the above problems, a genetic algorithm is proposed to optimise association rules, and then an incremental association rule mining algorithm is constructed by combining it with information entropy improved by mutual information method. The experimental results show that when processing the data set Q with a large amount of data, the speedup ratio of the PARIMIEG algorithm is better than other algorithms in different stages, the highest is 2.3, and the accuracy rate is 92.5%. The PARIMIEG algorithm can be applied to the performance index assessment of enterprises, personnel, and talent selection in subsequent human resource management. It is an excellent tool to improve the company's human resource management level and promote the development of the market economy.

Keywords: association rules; human resources; information entropy; technology fusion; genetic algorithm.

DOI: 10.1504/IJWET.2023.133617

International Journal of Web Engineering and Technology, 2023 Vol.18 No.3, pp.221 - 237

Received: 11 Oct 2022
Received in revised form: 13 Apr 2023
Accepted: 12 Jun 2023
Published online: 25 Sep 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article