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Mining Critical Least Association Rule from Oral Cancer Dataset

  • Conference paper
Recent Advances on Soft Computing and Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

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Abstract

Data mining has attracted many research attentions in the information industry. One of the important and interesting areas in data mining is mining infrequent or least association rule. Typically, least association rule is referred to the infrequent or uncommonness relationship among a set of item (itemset) in database. However, finding this rule is more difficult than frequent rule because they may contain only fewer data and thus require more specific measure. Therefore, in this paper we applied our novel measure called Critical Relative Support (CRS) to mine the critical least association rule from the medical dataset called Oral-Cancer-HUSM-S1. The result shows that CRS can be use to determine the least association rule and thus proven its scalability.

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Author information

Authors and Affiliations

  1. School of Informatics & Applied Mathematics, Universiti Malaysia Terengganu, Terengganu, Malaysia

    Zailani Abdullah, Fatiha Mohd & Md Yazid Mohd Saman

  2. Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

  3. Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  4. Faculty of Information Science & Technology, Universiti Kebangsaan, Bangi, Malaysia

    Abd Razak Hamdan

Authors
  1. Zailani Abdullah
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  2. Fatiha Mohd
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  3. Md Yazid Mohd Saman
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  4. Mustafa Mat Deris
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  5. Tutut Herawan
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  6. Abd Razak Hamdan
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Corresponding author

Correspondence to Zailani Abdullah .

Editor information

Editors and Affiliations

  1. Department of Information System Faculty of Comp. Sci. & Info. Tech., University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Comp. Sci. and Info. Tech, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Rozaida Ghazali

  3. Faculty of Comp. Sci. and Info. Tech., Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

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© 2014 Springer International Publishing Switzerland

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Abdullah, Z., Mohd, F., Mohd Saman, M.Y., Deris, M.M., Herawan, T., Hamdan, A.R. (2014). Mining Critical Least Association Rule from Oral Cancer Dataset. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_50

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_50

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

  • eBook Packages: EngineeringEngineering (R0)

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