Application of a test survey system based on cluster analysis and machine learning in the tasks of professional selection of specialists
Authors:   Lviv Polytechnic National University, Boiko Bohdan , Protsyk Iryna ,   Lviv Polytechnic National University
Published: January 2025

The research aims to develop a test survey for the effective selection of specialists in the field of IT, utilizing modern machine learning methods, particularly cluster analysis with the k-means algorithm. Due to limited access to existing testing platforms, which are typically available only to large companies on a paid basis, a decision was made to create an alternative web application. This application will serve as an accessible tool for a wider range of users and will automate the evaluation of candidates' qualifications.
The study involves designing a test survey specifically for recruiters in the IT field. Currently, similar tests are provided by the government but are usually accessible to companies only through subscriptions. To address this limitation, the alternative service—a web application—was proposed. The web application will leverage machine learning techniques, specifically the k-means clustering algorithm, to analyze user test results.
A key feature of this research is the application of cluster analysis to group users based on their professional skills, cognitive abilities, and psychological characteristics. In addition to the k-means algorithm, other clustering methods such as hierarchical clustering and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) were considered and tested to ensure robustness in grouping users. The combination of these methods enables both partitioning the data into clusters with predefined numbers of groups (via k-means) and identifying clusters with varying densities and shapes (via DBSCAN). Meanwhile, hierarchical clustering provides insights into relationships between data points at different levels of granularity. This approach allows for a more accurate assessment of candidates' suitability for employers’ requirements and facilitates better data organization for further analysis.
The study highlights the importance of cluster analysis in situations where there is no prior hypothesis about the data structure, making it a versatile tool for data classification. Beyond the technical aspects, the research explores the potential of using adaptive tests that adjust the difficulty level in real-time based on the user’s responses. This enhances the accuracy of the assessment and minimizes the influence of subjective factors during the selection process.
Furthermore, the study examines the potential for analyzing candidates’ behavioral and emotional characteristics, such as stress tolerance and communication skills, which are crucial for effective teamwork. The research confirmed the hypothesis that the testing challenge can be addressed by creating a web application capable of analyzing users' responses and providing detailed results. Based on these results, participants can evaluate their professional suitability for the IT field. Future developments may include the addition of new test categories and features if the system demonstrates its scalability and adaptability.