Title: A novel data cluster algorithm based on linear regression and residual analysis for human resource management
Authors: Hengxiaoyuan Wang
Addresses: School of Management, Dalian University of Finance and Economics, 116622, China
Abstract: Human resource management has become an important part of enterprise management. How to select high-quality talent and how to allocate corresponding talent to appropriate work has become an increasingly acute problem. Traditional data cluster methods cannot effectively solve the above problem due to the high-dimensional data. Therefore, we propose a novel data cluster algorithm based on linear regression and residual analysis for human resource management. Improved hybrid entropy weight attribute similarity is adopted for measuring the similarity between objects. The proposed local density calculation method based on k-nearest neighbour (KNN) and Parzen window is used to calculate the density of each object. Then, we utilise the linear regression and residual analysis to select the clustering centre points quickly and automatically, which can eliminate the subjectivity of artificial selection. A new clustering centre objective optimisation model is proposed to determine the real clustering centre. Through theoretical analysis and comparative experiments on artificial data sets and real data sets, it shows that the proposed cluster algorithm can overcome the defects of the original algorithms, and achieve better clustering effect and lower computation time than state-of-the-art methods.
Keywords: human resource management; data cluster; linear regression; residual analysis; clustering centre objective optimisation model.
International Journal of Applied Decision Sciences, 2022 Vol.15 No.6, pp.767 - 790
Received: 13 Aug 2021
Accepted: 01 Sep 2021
Published online: 11 Oct 2022 *