Analysis of the Behavior of Company Employees as Users of Various Systems or Tools, based on Employees Clustering with K-Means Algorithm
2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2023): Proceedings 2023
Pāvels Garkalns, Oksana Ņikiforova, Vitālijs Zabiņako, Jurijs Korņijenko

While analyzing individual user’s behavior in information systems or tools used for performing daily duties, identifying inefficient behavior scenarios can be challenging. To address these issues, it is necessary to automate the analysis and grouping of users behavior patterns using AI/ML and clustering mechanisms. With this approach, users can be classified and grouped based on their behavioral similarities. The K-means algorithm can be utilized as a clustering method to group employees with similar behavior and analyze their actions in comparison to the expected behavior of their respective groups. This allows for the identification of less efficient or ambiguous actions scenario and targeted training of according users. The proposed research aims to enhance the user’s grouping, improve behavioral analysis and optimize efficiency of work in information systems by introducing a user behavior model-driven analysis approach.


Atslēgas vārdi
behavior, clustering, information system users, k-means
DOI
10.1109/ITMS59786.2023.10317652
Hipersaite
https://ieeexplore.ieee.org/document/10317652

Garkalns, P., Ņikiforova, O., Zabiņako, V., Korņijenko, J. Analysis of the Behavior of Company Employees as Users of Various Systems or Tools, based on Employees Clustering with K-Means Algorithm. No: 2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2023): Proceedings, Latvija, Riga, 5.-6. oktobris, 2024. Piscataway: IEEE, 2023, 1.-7.lpp. ISBN 979-8-3503-7030-0. e-ISBN 979-8-3503-7029-4. ISSN 2771-6953. e-ISSN 2771-6937. Pieejams: doi:10.1109/ITMS59786.2023.10317652

Publikācijas valoda
English (en)
RTU Zinātniskā bibliotēka.
E-pasts: uzzinas@rtu.lv; Tālr: +371 28399196