Exploring Ethnic and Gender Patterns in Higher Education Enrollment: A Data Mining Approach

Abstract

This research delves into the analysis of student enrollment patterns in higher education programs across India for the year 2015. Utilizing a comprehensive dataset encompassing demographic details, census information, and features of various colleges and programs, three distinct data mining techniques are applied: Apriori rule mining, K-means clustering, and logistic regression classification. The findings reveal insightful relationships between different ethnic groups, genders, and educational preferences. The study not only showcases the potential of data mining in higher education analysis but also highlights the challenges and advantages of each technique on a large, sparse, and high-dimensional dataset.