Mining significant association rules from educational data using critical relative support approach

Mining significant association rules from educational data using critical relative support approach

Least association rules are the association rules that consist of the least item. These rules are very important and critical since they can be used to detect the infrequent events and exceptional cases. However, the formulation of measurement to efficiently discover least association rules is quite...

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Bibliographic Details
Main Authors: Zailani, Abdullah, Herawan, Tutut, Noraziah, Ahmad, Mustafa, Mat Deris
Format: Article
Language:English
Published: Elsevier Ltd. 2011
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/1/Mining%20significant%20association%20rules%20from%20educational%20data%20using%20critical%20relative%20support%20approach.pdf
id ump-24805
recordtype eprints
spelling ump-248052019-07-08T06:27:09Z http://umpir.ump.edu.my/id/eprint/24805/ Mining significant association rules from educational data using critical relative support approach Zailani, Abdullah Herawan, Tutut Noraziah, Ahmad Mustafa, Mat Deris QA76 Computer software Least association rules are the association rules that consist of the least item. These rules are very important and critical since they can be used to detect the infrequent events and exceptional cases. However, the formulation of measurement to efficiently discover least association rules is quite intricate and not really straight forward. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. Therefore, this paper proposes a new measurement called Critical Relative Support (CRS) to mine critical least association rules from educational context. Experiment with students’ examination result dataset shows that this approach can be used to reveal the significant rules and also can reduce up to 98% of uninterested association rules. Elsevier Ltd. 2011 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24805/1/Mining%20significant%20association%20rules%20from%20educational%20data%20using%20critical%20relative%20support%20approach.pdf Zailani, Abdullah and Herawan, Tutut and Noraziah, Ahmad and Mustafa, Mat Deris (2011) Mining significant association rules from educational data using critical relative support approach. Procedia - Social and Behavioral Sciences, 28. pp. 97-101. ISSN 1877-0428, ESSN: 1877-0428 https://doi.org/10.1016/j.sbspro.2011.11.020 https://doi.org/10.1016/j.sbspro.2011.11.020
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Zailani, Abdullah
Herawan, Tutut
Noraziah, Ahmad
Mustafa, Mat Deris
Mining significant association rules from educational data using critical relative support approach
description Least association rules are the association rules that consist of the least item. These rules are very important and critical since they can be used to detect the infrequent events and exceptional cases. However, the formulation of measurement to efficiently discover least association rules is quite intricate and not really straight forward. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. Therefore, this paper proposes a new measurement called Critical Relative Support (CRS) to mine critical least association rules from educational context. Experiment with students’ examination result dataset shows that this approach can be used to reveal the significant rules and also can reduce up to 98% of uninterested association rules.
format Article
author Zailani, Abdullah
Herawan, Tutut
Noraziah, Ahmad
Mustafa, Mat Deris
author_facet Zailani, Abdullah
Herawan, Tutut
Noraziah, Ahmad
Mustafa, Mat Deris
author_sort Zailani, Abdullah
title Mining significant association rules from educational data using critical relative support approach
title_short Mining significant association rules from educational data using critical relative support approach
title_full Mining significant association rules from educational data using critical relative support approach
title_fullStr Mining significant association rules from educational data using critical relative support approach
title_full_unstemmed Mining significant association rules from educational data using critical relative support approach
title_sort mining significant association rules from educational data using critical relative support approach
publisher Elsevier Ltd.
publishDate 2011
url http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/
http://umpir.ump.edu.my/id/eprint/24805/1/Mining%20significant%20association%20rules%20from%20educational%20data%20using%20critical%20relative%20support%20approach.pdf
first_indexed 2023-09-18T22:37:46Z
last_indexed 2023-09-18T22:37:46Z
_version_ 1777416710210453504

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