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CNAR-M: A Model for Mining Critical Negative Association Rules

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Computational Intelligence and Intelligent Systems (ISICA 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 316))

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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.

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Author information

Authors and Affiliations

  1. Faculty of Computer System and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Kuantan, Pahang, Malaysia

    Tutut Herawan

  2. Department of Computer Science, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, Terengganu, Malaysia

    Zailani Abdullah

Authors
  1. Tutut Herawan
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  2. Zailani Abdullah
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Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, China

    Zhenhua Li  & Xiang Li  & 

  2. School of Computer Science and Engineering, The University of Aizu, Aizu, Japan

    Yong Liu

  3. School of Computer Science, China University of Geosciences, 430074, Wuhan, China

    Zhihua Cai

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© 2012 Springer-Verlag Berlin Heidelberg

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Herawan, T., Abdullah, Z. (2012). CNAR-M: A Model for Mining Critical Negative Association Rules. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_20

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  • DOI: https://doi.org/10.1007/978-3-642-34289-9_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34288-2

  • Online ISBN: 978-3-642-34289-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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