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  • 标题:An Approach for Integrating Data Mining with Saudi Universities Database Systems: Case Study
  • 本地全文:下载
  • 作者:Mohamed Osman Hegazi ; Mohammad Alhawarat ; Anwer Hilal
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2016
  • 卷号:7
  • 期号:6
  • DOI:10.14569/IJACSA.2016.070627
  • 出版社:Science and Information Society (SAI)
  • 摘要:This paper presents an approach for integrating data mining algorithms within Saudi university’s database system, viz., Prince Sattam Bin Abdulaziz University (PSAU) as a case study. The approach based on a bottom-up methodology; it starts by providing a data mining application that represents a solution to one of the problems that face Saudi Universities’ systems. After that, it integrates and implements the solution inside the university’s database system. This process is then repeated to enhance the university system by providing data mining tools that help different parties -especially decision makers- to carry out certain decision. The paper presents a case study that includes analyzing and predicting the student withdrawal from courses at PSAU using association rule mining, neural networks and decision trees. Then it provides a conceptual and practical approach for integrating the resulted application within the university’s database system. The experiment improves that this approach can be used as a framework for integrating data mining techniques within Saudi university’s database systems. The paper concluded that mining universities’ data can be applied as a computer system (intelligent university’s system), Also, data mining algorithms can be adapted with any database system regardless that this system is new, exists or legacy. Moreover, data mining algorithms can be a solution for some educational problems, in addition to providing information for decision makers and users
  • 关键词:thesai; IJACSA; thesai.org; journal; IJACSA papers; Data Mining; Database; Predict; Integration; Association rule mining; Neural networks; Decision tree; Educational Data Mining (EDM); University system
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