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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References
A. Totohasina, D. Feno, De la qualité des règles d’association: étude comparative des meures MGK et Confiance Actes du 9ème colloque Africain sur la recherche en Informatique et Mathématiques Appliquées, CARI-2008, pp. 561–568
R. Gras, J.-C. Régnier, C. Marinica, F. Guillet, L’Analyse Statistique Implicative- Méthode exploratoire et confirmatoire à la recherche de causalités, Cépaduès; édition : 2e édition revue et augmentée (2013). ISBN-13: 978-2364930568
H.F. Rakotomalala, A. Totohasina, J. Diatta, Extraction des rè gles d’associations Mgk-valides avec contribution de Support, Actes des 24èmes rencontres de la Société Francophone de Classification SFC 2017, Lyon, France, 2017, pp. 29–32
H.F. Rakotomalala, A. Totohasina, J. Diatta, Une mesure de cohésion basée sur la mesure de qualité des règles d’association Mgk, Actes des 24èmes rencontres de la Société Francophone de Classification SFC 2017, Lyon, France, 2017, pp. 21–24
H.F. Rakotomalala, A. Totohasina, An efficient new cohesion indice based on the quality measure of association rules Mgk, WorldS4 2018, in 2nd World Conference on Smart Trends in System (Security & Sustainability, IEEE-UK, London, 2018)
H.F. Rakotomalala, B. Ralahady, A. Totohasina, A novel cohesitive implicative classiffication based on mgk and application on diagnostic on informatics literacy of students of higher education in madagascar, in 3rd International Conference ICICT 2018-International Congress & Excellence Awards, London 2018. Advances in Intelligent Systems and Computing, vol. 797 (Springer, 2018), pp. 161–174
R. Shackelford, J. Cross, G. Davies, J. Impagliazzo, R. Kamali, R. LeBlanc, B. Lunt, A. McGettrick, R. Sloan, H Topi The Overview Report covering undergraduate degree programs, in CE-CS-IS-IT-SE, CC, 2005 (New York, 2005). ISBN 1-59593-359-X
H.F. Rakotomalala, A. Totohasina, J. Diatta, Classification des mesures des règles d’association selon CHIC-Mgk, Actes des 25èmes rencontres de la Société Francophone de Classification SFC 2018 (Paris Descartes, France, 2018)
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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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