Mining Least Association Rules of Degree Level Programs Selected by Students
One of the most popular and important studies in data mining is association rules mining. Generally, association rules can be divided into two categories called frequent and least. However, finding the least association rules is more complex and time consuming as compared to the frequent one. These...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SERSC
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6614/1/23.pdf http://umpir.ump.edu.my/id/eprint/6614/ http://www.sersc.org/journals/IJMUE/vol9_no1_2014/23.pdf |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | One of the most popular and important studies in data mining is association rules mining. Generally, association rules can be divided into two categories called frequent and least. However, finding the least association rules is more complex and time consuming as compared to the frequent one. These rules are very useful in certain application domain such as determining the exceptional association between university’s programs being selected by students. Therefore in this paper, we apply our novel measure called Definite Factors (DF) to determine the significant least association rules from undergraduate’s program selection database. The dataset of computer science student for July 2008/2009 intake from Universiti Malaysia Terengganu was employed in the experiment. The result shows that our measurement can mine these rules and it is at par with the existing benchmarked Relative Support Apriori (RSA) measurement. |
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