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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Main Authors:	Zailani, Abdullah, Noraziah, Ahmad, Mustafa, Mat Deris, Rozaida, Ghazali, Herawan, Tutut
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/6616/ http://umpir.ump.edu.my/id/eprint/6616/ http://umpir.ump.edu.my/id/eprint/6616/1/85840783.pdf

id	ump-6616
recordtype	eprints
spelling	ump-66162018-02-02T07:26:47Z http://umpir.ump.edu.my/id/eprint/6616/ Mining Indirect Least Association Rule from Students' Examination Datasets Zailani, Abdullah Noraziah, Ahmad Mustafa, Mat Deris Rozaida, Ghazali Herawan, Tutut QA75 Electronic computers. Computer science 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. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6616/1/85840783.pdf 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.. http://www.springer.com/computer/communication+networks/book/978-3-319-09146-4
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Zailani, Abdullah Noraziah, Ahmad Mustafa, Mat Deris Rozaida, Ghazali Herawan, Tutut Mining Indirect Least Association Rule from Students' Examination Datasets
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.
format	Conference or Workshop Item
author	Zailani, Abdullah Noraziah, Ahmad Mustafa, Mat Deris Rozaida, Ghazali Herawan, Tutut
author_facet	Zailani, Abdullah Noraziah, Ahmad Mustafa, Mat Deris Rozaida, Ghazali Herawan, Tutut
author_sort	Zailani, Abdullah
title	Mining Indirect Least Association Rule from Students' Examination Datasets
title_short	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_sort	mining indirect least association rule from students' examination datasets
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/6616/ http://umpir.ump.edu.my/id/eprint/6616/ http://umpir.ump.edu.my/id/eprint/6616/1/85840783.pdf
first_indexed	2023-09-18T22:02:33Z
last_indexed	2023-09-18T22:02:33Z
_version_	1777414494923784192

Mining Indirect Least Association Rule from Students' Examination Datasets

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