Dynamic Matching Algorithm of Human Resource Allocation Based on Big Data Mining

Abstract

In order to ensure the dynamic matching effect of human resources allocation and improve the accuracy and efficiency of dynamic matching of human resources allocation, a dynamic matching algorithm of human resources allocation based on big data mining is studied. Analyze the meaning and function of big data mining, and explain the common analysis principles of big data mining. The information entropy is selected as the basis for measuring human resource allocation, the human resource allocation is extracted, and the similarity of human resource allocation is calculated using the Huasdorff similarity method based on time interpolation. According to the Apriori algorithm and FP-Growth classification algorithm, the human resource allocation is classified and mined, and the K-Means clustering algorithm is used to realize the dynamic matching of human resource allocation. The experimental results show that the proposed algorithm has better dynamic matching effect of human resources allocation, and can effectively improve the accuracy and efficiency of dynamic matching of human resources allocation.