Extracting highly positive association rules from students' enrollment data
Association 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 a...
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| Format: | Article |
| Language: | en |
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Elsevier Ltd.
2011
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24799/1/Extracting%20highly%20positive%20association%20rules%20from%20students.pdf http://umpir.ump.edu.my/id/eprint/24799/ https://doi.org/10.1016/j.sbspro.2011.11.022 |
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| author | Zailani, Abdullah Herawan, Tutut Noraziah, Ahmad Mustafa Mohamed, Mat Deris |
| author_facet | Zailani, Abdullah Herawan, Tutut Noraziah, Ahmad Mustafa Mohamed, Mat Deris |
| author_sort | Zailani, Abdullah |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Association 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. |
| format | Article |
| id | my.ump.umpir.24799 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2011 |
| publisher | Elsevier Ltd. |
| record_format | eprints |
| spelling | my.ump.umpir.247992019-06-12T03:51:25Z http://umpir.ump.edu.my/id/eprint/24799/ Extracting highly positive association rules from students' enrollment data Zailani, Abdullah Herawan, Tutut Noraziah, Ahmad Mustafa Mohamed, Mat Deris QA76 Computer software Association 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. Elsevier Ltd. 2011 Article PeerReviewed pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/24799/1/Extracting%20highly%20positive%20association%20rules%20from%20students.pdf Zailani, Abdullah and Herawan, Tutut and Noraziah, Ahmad and Mustafa Mohamed, Mat Deris (2011) Extracting highly positive association rules from students' enrollment data. Procedia - Social and Behavioral Sciences, 28. pp. 107-111. ISSN 1877-0428, ESSN: 1877-0428. (Published) https://doi.org/10.1016/j.sbspro.2011.11.022 https://doi.org/10.1016/j.sbspro.2011.11.022 |
| spellingShingle | QA76 Computer software Zailani, Abdullah Herawan, Tutut Noraziah, Ahmad Mustafa Mohamed, Mat Deris Extracting highly positive association rules from students' enrollment data |
| title | Extracting highly positive association rules from students' enrollment data |
| title_full | Extracting highly positive association rules from students' enrollment data |
| title_fullStr | Extracting highly positive association rules from students' enrollment data |
| title_full_unstemmed | Extracting highly positive association rules from students' enrollment data |
| title_short | Extracting highly positive association rules from students' enrollment data |
| title_sort | extracting highly positive association rules from students' enrollment data |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/24799/1/Extracting%20highly%20positive%20association%20rules%20from%20students.pdf http://umpir.ump.edu.my/id/eprint/24799/ https://doi.org/10.1016/j.sbspro.2011.11.022 https://doi.org/10.1016/j.sbspro.2011.11.022 |
| url_provider | http://umpir.ump.edu.my/ |