<![CDATA[University students generate extensive digital footprints that form complex, high-dimensional datasets reflecting diverse patterns of online behavior. Understanding these patterns requires analytical methods capable of handling large and interrelated variables. This study aims to map internet usage trends among university students using an integrated approach that combines Principal Component Analysis (PCA) and the Apriori Algorithm. PCA is employed to reduce data dimensionality by identifying the most influential components and eliminating redundant information, thereby simplifying the dataset without losing essential characteristics. Subsequently, the Apriori Algorithm is applied to uncover association rules that describe relationships between different types of digital activities. Data were collected through structured questionnaires distributed to active university students, capturing various aspects of their internet usage behavior. Through this combined methodology, the study seeks to identify the main factors that shape students’ digital habits and to reveal hidden behavioral patterns that may not be evident through conventional analysis. The expected results will provide a clearer understanding of how students interact with digital platforms and online resources. Ultimately, the findings are intended to serve as an empirical basis for designing more effective, data-driven digital literacy programs and strategies in higher education environments.]]>
