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  • 标题:Usage of Measures of Interestingness in Educational Data
  • 本地全文:下载
  • 作者:Monika Srivastava ; Sweta Gupta
  • 期刊名称:International Journal of Electronics Communication and Computer Technology
  • 印刷版ISSN:2249-7838
  • 出版年度:2011
  • 卷号:1
  • 期号:2
  • 页码:30
  • 出版社:International Journal of Electronics Communication and Computer Technology
  • 摘要:With the growing amount of data a new field called data mining is emerging extremely quickly. Data mining tools which perform data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific, educational and medical research. Association rules mining is one of the most well studied data mining tasks. It discovers relationships among attributes in databases, producing if-then statements concerning attribute-values. There are increasing research interests in using data mining in education. This new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data come from educational environments. This paper shows how data mining can be used to come up with interesting knowledge from student database. We identify the measure of interestingness and implement them on student database and come up with interesting rules that can help teachers and related people to deal with students and understand their behavior and activities
  • 关键词:mining;educational data;association rule; ;support; confidence
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