Student Mining Using K-Means Clustering: A Basis for Improving Higher Education Marketing Strategies

Melanie  Arpay,

Student Mining Using K-Means Clustering: A Basis for Improving Higher Education Marketing Strategies

Discipline: Education

Abstract:

This study aims to enhance marketing strategies in higher education institutions by applying data mining techniques, specifically K-means clustering. The research focuses on Mindanao State University - Lanao del Norte Agricultural College (MSU-LNAC), a tertiary institution in Northern Mindanao, Philippines, with the objective of increasing enrollment. The study utilizes the K-means algorithm to group attributes into different clusters. The clustering analysis provides valuable insights into the characteristics and preferences of the surveyed student population. Based on the findings, recommendations are presented to guide targeted marketing efforts, such as geographic targeting, collaborations with senior high schools, financial assistance programs, and the development of marketing campaigns that emphasize the institution's strengths and advantages. By implementing these recommendations, MSU-LNAC can enhance its recruitment and marketing strategies to attract and retain students effectively.

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