基于层次聚类分析的也门地区教育数据挖掘

F. Alqasemi, Salah Al-Hagree, Amal M. Aqlan, Khaled M. Alalayah, Zahraa Almotwakl, Mohammed Hadwan
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引用次数: 1

摘要

教育数据挖掘(Educational Data Mining, EDM)是近年来发展起来的一个新兴领域,主要用于提取教育领域内在的新事实。EDM已成为教育信息学领域的研究热点。本文将聚类分析应用于也门地区教育统计。我们使用分层算法实现了一个挖掘过程。聚类分析描述教育数据下的潜在知识,用树状图表示;即层次图。通过执行单链接方法,我们使用教育数据分析对也门地区进行了分类。利用这种分类方法生成层次排序,从而得到目标领域隐含知识的总体图像。研究结果显示了也门各地区之间有希望的关系,这将有助于决策者了解分布在全国各地的教育变量的性质。
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Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis
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.
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