文章基本信息

标题：Extracting highly positive association rules from students’ enrollment data
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作者：Zailani Abdullah ; Zailani Abdullah ; Tutut Herawan 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2011
卷号：28
页码：107-111
DOI：10.1016/j.sbspro.2011.11.022
语种：English
出版社：Elsevier
摘要：AbstractAssociation Rules Mining is one of the popular techniques used in data mining. Positive association rules are very useful in correlation analysis and decision making processes. In educational context, determine a “right” program to the students is very unclear especially when their chosen programs are not selected. In this case, normally they will be offered to other programs based on the programs availability and not according to their program's field interests. The main concern is, by assigning inappropriate program which is not reflected their overall interest; it may create serious problems such as poorly in academic commitment and academic achievement. Therefore, Therefore in this paper, we proposed a model which consists of pre-processing, mining patterns and assigning weight to discover highly positive association rules. We examined the previous chosen programs by computer science students in our university for July 2008/2009 intake. The result shows that the proposed model can mine association rules with high correlation. Moreover, for data analysis, there are existed students that have been offered in computer science program at our university but not within their program's field interests.
关键词：Positive association rules;Educational;Progams