A COMPARISON OF THE USE OF K-MEANS AND PRINCIPAL COMPONENTS (PCA) METHODS IN CLUSTERING ENGINEERING CVS

UDC 331.523
DOI: 10.36871/ek.up.p.r.2024.10.06.003

Authors

Ilshat Z. Garafiev,
Kazan National Research Technological University, Kazan, Republic of Tatarstan, Russia

Abstract

This paper provides a comparative analysis of two methods for clustering engineers’ CVs: K-means and the principal component analysis (PCA). The study is based on data from the “Work in Russia” portal, including CVs. The main parameters for clustering were the desired salary level, age, work experience and gender of applicants. The results of the study showed that the PCA method provides a higher quality of clustering; both methods demonstrated a high degree of cluster coincidence.

Keywords

labor market, engineers’ CVs, clustering, k-means method, principal component analysis.