GSP Internet Users Based on Their Navigation Preferences: Second Round-Law Sentences

This article presents an exploration into data mining methodologies applied to discerning prevalent navigation behaviors in educational settings. The study delves into the methodology employed by selected data mining algorithms to categorize Internet users based on their browsing preferences. Additionally, it elaborates on the training data used for these algorithms, offering insights into their efficiency in classification. The research aims to uncover recurrent behavioral sequences among users with similar preferences, facilitating the development of mechanisms for seamless categorization of new users based on established patterns. The GSP_M algorithm, an innovative approach detailed in this study, is employed for in-depth analysis, especially for users exhibiting divergent browsing behaviors. This article contributes to understanding the intricate patterns of web navigation within diverse user groups and proposes methods to enhance categorization accuracy for tailored learning environments.