Clustering-Based Students' Descriptive Model

When students enroll at universities, various datasets can be available to managers and teachers. Clustering techniques can be applied in order to divide the instances within those datasets into natural groups. In this paper, one clustering-based approach combined with attribute selection methods for identifying specific input dataset variables meaningful for the disjunction of distinct students' profiles has been proposed. Also, an analysis of the descriptive students' model obtained by the proposed methodology is performed.