Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis

Autor: Amal Aqlan, Fahd Alqasemi, Zahraa Almotwakl, Salah Al-Hagree, Mohammed Hadwan, Khaled M. A. Alalayah
Rok vydání: 2021
Předmět:
Zdroj: 2021 International Conference of Technology, Science and Administration (ICTSA).
DOI: 10.1109/ictsa52017.2021.9406544
Popis: In recent years, Educational Data Mining (EDM) is a new field that has been employed for extracting intrinsic educational new facts. EDM has become a hot topic in the field of educational informatics. In this paper we had applied clustering analysis on Yemen regions education statistics. We had achieved a mining process using hierarchical algorithm. The clustering analysis depicts latent knowledge beneath education data, which is illustrated by a dendrogram; i.e. hierarchical diagram. By performing single-linkage method, we had categorized Yemen regions using education data analysis. This categorization is employed for generating hierarchical ranking, which draw general image of the implied knowledge of targeted domain. The results presents promising relations between Yemen regions, that would help decision makers to understand the nature of education variables, which are distributed over the country.
Databáze: OpenAIRE