Mining Indirect Least Association Rule from Students' Examination Datasets

Zailani, Abdullah and Noraziah, Ahmad and Mustafa, Mat Deris and Rozaida, Ghazali and Herawan, Tutut (2014) Mining Indirect Least Association Rule from Students' Examination Datasets. In: Proceedings of the 14th International Conference on Computational Science and Its Applications (ICCSA 2014) , 30 June - 3 July 2014 , University of Minho, Guimaraes, Portugal. pp. 783-797..

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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.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Proceedings, Part IV Series: Lecture Notes in Computer Science, Vol. 8582 Subseries: Theoretical Computer Science and General Issues ISBN: 978-3-319-09147-1 Publisher:Springer International Publishing Switzerland 2014
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: PM Dr. Noraziah Ahmad
Date Deposited: 22 Sep 2014 08:11
Last Modified: 02 Feb 2018 07:26
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