USING CLUSTERING ALGORITHMS FOR COMPANY PERSONNEL SEGMENTATION

Abstract

This research thoroughly examines the importance and potential of applying clustering algorithms in the context of human resource management. It is noted that personnel segmentation is a strategic component for companies of any size and industry, as it allows for more effective adaptation of management strategies to the needs and characteristics of different groups of employees. It is emphasized that clustering algorithms can automate the segmentation process, providing objective and consistent results. Various types of clustering algorithms are described in detail, including their principles of operation and application peculiarities in the context of personnel management. In particular, it is highlighted how clustering can be based on different criteria such as skills, professional experience, motivation, and others, and how these clusters can be used to improve hiring processes, career planning, performance evaluation, and personnel development. Successful case examples of implementing clustering algorithms in human resource management practices are also provided, demonstrating their positive impact on company efficiency and strategic development. Finally, the need for further research in this area is emphasized to refine personnel segmentation methodologies and expand their application in various business sectors. This research conducted a cluster analysis of a dataset with marketing data using three different clustering methods: k-means, DBSCAN, and agglomerative clustering. The results of the evaluation showed that DBSCAN demonstrated the best performance both with the full and reduced dataset, indicating its effectiveness in identifying densely populated clusters with irregular distributions of points. In contrast, the k-means method proved to be less effective for data with irregular distributions and outliers. Agglomerative clustering yielded moderate results but was found to be vulnerable to outliers and demanding in terms of computational resources. Overall, the study allows us to conclude on the effectiveness of different clustering methods in identifying data structures in marketing research.