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, Tutut, Herawan, Noraziah, Ahmad, Rozaida, Ghazali, Mustafa, Mat Deris
Format:	Article
Published:	Springer International Publishing 2014
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/8794/ http://umpir.ump.edu.my/id/eprint/8794/ http://umpir.ump.edu.my/id/eprint/8794/

id	ump-8794
recordtype	eprints
spelling	ump-87942018-02-02T07:28:05Z 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) http://dx.doi.org/10.1007/978-3-319-09153-2_58 http://www.waset.org/publications/10000510
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
topic	QA76 Computer software
spellingShingle	QA76 Computer software Zailani, Abdullah Tutut, Herawan Noraziah, Ahmad Rozaida, Ghazali Mustafa, Mat Deris 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	Article
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
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
publisher	Springer International Publishing
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/8794/ http://umpir.ump.edu.my/id/eprint/8794/ http://umpir.ump.edu.my/id/eprint/8794/
first_indexed	2023-09-18T22:06:45Z
last_indexed	2023-09-18T22:06:45Z
_version_	1777414759509917696

Mining Indirect Least Association Rule from Students’ Examination Datasets

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