Analysis of the Behavior of Company Employees as Users of Information Systems or Tools, Based on Employees Clustering with K-means Algorithm

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.