Research on the Construction of Portrait of Higher Vocational College Students' Groups Based on Clustering Ensemble

Abstract

The rapid development of higher vocational education necessitates personalized teaching tailored to individual students. This paper presents an ensemble optimized clustering framework to classify higher vocational college students accurately. By integrating multiple clustering algorithms and employing optimization mechanisms, the framework enhances clustering quality. Experiments on real student data show that this method excels in clustering performance and adapts to various feature data types, yielding results aligned with actual situations. This research provides a data foundation for personalized education in higher vocational colleges, promoting educational equity and cultivating skilled talents.