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

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.