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 \(M_{GK}\). 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.
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Rakotomalala, H.F., Totohasina, A. (2020). On Hierarchical Classification Implicative and Cohesive \(M_{GK}\)-Based: Application on Analysis of the Computing Curricula and Students Abilities According the Anglo-Saxon Model. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_7
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