On hierarchical classification implicative and cohesive mgk-based: Application on analysis of the computing curricula and students abilities according the anglo-saxon model

Abstract

Extracting association rules from a huge binary data according to a quality measure is an important pretreatment step in data analysis. Also, among unsupervised techniques, our approach for a hierarchical classification implicative and cohesive is based on the new measure of cohesion according to the interestigness measure MGK. In this paper, we present, for the first time, a validation of this approach in the field of education, mainly in the computing curricula and the performance capabilities of students pursuing this curriculum in the Anglo-Saxon model.