Mining Critical Least Association Rule from Oral Cancer Dataset

  • Zailani Abdullah
  • Fatiha Mohd
  • Md Yazid Mohd Saman
  • Mustafa Mat Deris
  • Tutut Herawan
  • Abd Razak Hamdan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 287)

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.

Keywords

Critical least association rules medical dataset 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zailani Abdullah
    • 1
  • Fatiha Mohd
    • 1
  • Md Yazid Mohd Saman
    • 1
  • Mustafa Mat Deris
    • 2
  • Tutut Herawan
    • 3
  • Abd Razak Hamdan
    • 4
  1. 1.School of Informatics & Applied MathematicsUniversiti Malaysia TerengganuTerengganuMalaysia
  2. 2.Faculty of Science Computer and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  3. 3.Faculty of Computer Science & Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  4. 4.Faculty of Information Science & TechnologyUniversiti KebangsaanBangiMalaysia

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