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CNAR-M: A model for mining critical negative association rules

Herawan, Tutut and Zailani, Abdullah (2012) CNAR-M: A model for mining critical negative association rules. In: 6th International Symposium on Intelligence Computation and Applications (ISICA 2012), 27-28 October 2012 , Wuhan, China. pp. 170-179., 316. ISBN 978-3-642-34289-9

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Abstract

Association rules mining has been extensively studied in various multidiscipline applications. One of the important categories in association rule is known as Negative Association Rule (NAR). Significant NAR is very useful in certain domain applications; however it is hardly to be captured and discriminated. Therefore, in this paper we proposed a model called Critical Negative Association Rule Model (CNAR-M) to extract the Critical Negative Association Rule (CNAR) with higher Critical Relative Support (CRS) values. The result shows that the CNAR-M can mine CNAR from the benchmarked and real datasets. Moreover, it also can discriminate the CNAR with others association rules.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by scopus
Uncontrolled Keywords: Data Mining; Negative; Association Rules; Critical Relative Support
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 23 Mar 2020 02:32
Last Modified: 23 Mar 2020 02:32
URI: http://umpir.ump.edu.my/id/eprint/27009
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