<![CDATA[The quick advancement of information technology has made online shopping via e-commerce sites a component of students' lives. As active internet users, information systems students have a wide range of traits and factors to consider when making online purchases. The objective of this research is to categorize students from the 2023 cohort of Information Systems students based on behavioral similarities utilizing the K-Means Clustering approach and to assess the variables affecting their online purchasing patterns. Data mining methodologies are used in this study's quantitative approach. The survey, which was answered by 100 participants, was used to gather the data online. Price, discounts, product reviews, customer feedback, product photos and descriptions, product comparisons, and purchase satisfaction are among the variables studied. The findings suggest that there should be three clusters. Price and promotion-sensitive consumers are represented by the first cluster, while the second cluster centers on quality and satisfaction, and the third is made up of rational and thorough shoppers. According to these findings, K-Means Clustering can be used to objectively divide students' online shopping behavior.]]>
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