Researchers use K-means clustering to group 3,000+ college students by traits for personalized career guidance

Study analyzes student data including CET-4 exam scores, GPA, personality traits, and cadre experience to identify career fit

K-means algorithm classifies students into four distinct groups based on similar characteristic combinations

Approach addresses gap in existing research by focusing on student-career fitness rather than just predicting career paths

Algorithm minimizes intra-cluster squared error to ensure high similarity of characteristics within each student group