Mr. Marina A. Trenina Profile

Marina A. Trenina

at Togliatti State Univ.

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Proceedings Article | 18 May 2022 Paper

Data mining in building a stable HR management model

Oksana Gushchina, Evgeniya Zhelnina, Marina Trenina, Ekaterina Panyukova, Natalia Rogova

Proceedings Volume 12251, 122510S (2022) https://doi.org/10.1117/12.2630882

KEYWORDS: Data modeling, Data mining, Statistical modeling, Data analysis, Modeling, Machine learning, Evolutionary algorithms, Systems modeling, Neural networks

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Nowadays it is quite a common practice for modern enterprises to adopt data mining in human resource management. HR management can make use of data mining technologies to efficiently monitor the employees performing specific functions. However, today there are no approaches to integrating big data analysis methods into HR management processes. We have identified the most vulnerable HR management process: the stable staff allocation by scope of the tasks depending on their professional preferences and professional skills parameters. We suggest a solution for this problem which involves applying data mining methods to build a stable human resource management model. The model is based on employees' competence characteristics needed for performing professional tasks, as well as on assessment of staff engagement and productivity level. The model features a system of work activities which is provided with a set of conditions required for their execution and the employees' competences. Testing of the model's operability and stability engages the algorithm for staff allocation to professional tasks. The algorithm is based on cluster analysis which is used to identify clusters with dominant indexes and free elements with implicit characteristics. The linear regression method is applied to measure the degree of interconnectedness between the staff engagement and competence indexes for modelling the prospective dependence of these parameters. This became the defining factor for assessing the stability of the model in conditions of external destabilizing impact. An experiment was conducted to prove that the model is able to redistribute the free elements in order to maintain the system stability even in case of the initial data sample change.

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