Mining Indirect Least Association Rule from Students’ Examination Datasets
Association rule mining (ARM) is one of the most important and well researched area in data mining. Indirect association rule, a part of ARM, provides a different perspective in identifying the most useful infrequent patterns. Specifically, it refers to the property of high dependencies between two...
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| Format: | Article |
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Springer International Publishing
2014
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| _version_ | 1796990505525968896 |
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| author | Zailani, Abdullah Tutut, Herawan Noraziah, Ahmad Rozaida, Ghazali Mustafa, Mat Deris |
| author_facet | Zailani, Abdullah Tutut, Herawan Noraziah, Ahmad Rozaida, Ghazali Mustafa, Mat Deris |
| author_sort | Zailani, Abdullah |
| collection | UMP |
| description | Association rule mining (ARM) is one of the most important and well researched area in data mining. Indirect association rule, a part of ARM, provides a different perspective in identifying the most useful infrequent patterns. Specifically, it refers to the property of high dependencies between two items that are rarely appeared together but indirectly occurred through another items. Besides generating nontrivial information, it also can implicitly reveal a new fact of relationship which cannot be directly determined using the typical interestingness measures. Therefore, in this paper we applied our novel algorithm called Mining Lease Association Rule (MILAR) and our measure called Critical Relative Support (CRS) to mine the indirect least association rule from the students’ examination datasets. The experimental results show that the numbers of extracted indirect association rules are reduced when the threshold value of CRS is increased. This number is also lesser than the least association rule. In addition of decreasing the number of uninteresting rules, the obtained information also can be used by educators as a basis to improve their teaching and learning strategies in the future. |
| first_indexed | 2024-03-06T11:52:35Z |
| format | Article |
| id | UMPir8794 |
| institution | Universiti Malaysia Pahang |
| last_indexed | 2024-03-06T11:52:35Z |
| publishDate | 2014 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | UMPir87942018-02-02T07:28:05Z http://umpir.ump.edu.my/id/eprint/8794/ Mining Indirect Least Association Rule from Students’ Examination Datasets Zailani, Abdullah Tutut, Herawan Noraziah, Ahmad Rozaida, Ghazali Mustafa, Mat Deris QA76 Computer software Association rule mining (ARM) is one of the most important and well researched area in data mining. Indirect association rule, a part of ARM, provides a different perspective in identifying the most useful infrequent patterns. Specifically, it refers to the property of high dependencies between two items that are rarely appeared together but indirectly occurred through another items. Besides generating nontrivial information, it also can implicitly reveal a new fact of relationship which cannot be directly determined using the typical interestingness measures. Therefore, in this paper we applied our novel algorithm called Mining Lease Association Rule (MILAR) and our measure called Critical Relative Support (CRS) to mine the indirect least association rule from the students’ examination datasets. The experimental results show that the numbers of extracted indirect association rules are reduced when the threshold value of CRS is increased. This number is also lesser than the least association rule. In addition of decreasing the number of uninteresting rules, the obtained information also can be used by educators as a basis to improve their teaching and learning strategies in the future. Springer International Publishing 2014 Article PeerReviewed Zailani, Abdullah and Tutut, Herawan and Noraziah, Ahmad and Rozaida, Ghazali and Mustafa, Mat Deris (2014) Mining Indirect Least Association Rule from Students’ Examination Datasets. Computational Science and Its Applications, 8584. pp. 783-797. ISSN 978-3-319-09152-5 (ISBN). (Published) http://dx.doi.org/10.1007/978-3-319-09153-2_58 http://www.waset.org/publications/10000510 |
| spellingShingle | QA76 Computer software Zailani, Abdullah Tutut, Herawan Noraziah, Ahmad Rozaida, Ghazali Mustafa, Mat Deris Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title | Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title_full | Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title_fullStr | Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title_full_unstemmed | Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title_short | Mining Indirect Least Association Rule from Students’ Examination Datasets |
| title_sort | mining indirect least association rule from students examination datasets |
| topic | QA76 Computer software |
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