Mining critical least association rule from oral cancer dataset

Zailani Abdullah, Fatiha Mohd, Md Yazid Mohd Saman, Mustafa Mat Deris, Tutut Herawan, Abdul Razak Hamdan

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages529-538
Number of pages10
Volume287
ISBN (Print)9783319076911
DOIs
Publication statusPublished - 2014
Event1st International Conference on Soft Computing and Data Mining, SCDM 2014 - Parit Raja, Batu Pahat
Duration: 16 Jun 201418 Jun 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume287
ISSN (Print)21945357

Other

Other1st International Conference on Soft Computing and Data Mining, SCDM 2014
CityParit Raja, Batu Pahat
Period16/6/1418/6/14

Fingerprint

Association rules
Data mining
Scalability
Industry

Keywords

  • Critical
  • Least association rules
  • Medical dataset

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Abdullah, Z., Mohd, F., Mohd Saman, M. Y., Mat Deris, M., Herawan, T., & Hamdan, A. R. (2014). Mining critical least association rule from oral cancer dataset. In Advances in Intelligent Systems and Computing (Vol. 287, pp. 529-538). (Advances in Intelligent Systems and Computing; Vol. 287). Springer Verlag. https://doi.org/10.1007/978-3-319-07692-8_50

Mining critical least association rule from oral cancer dataset. / Abdullah, Zailani; Mohd, Fatiha; Mohd Saman, Md Yazid; Mat Deris, Mustafa; Herawan, Tutut; Hamdan, Abdul Razak.

Advances in Intelligent Systems and Computing. Vol. 287 Springer Verlag, 2014. p. 529-538 (Advances in Intelligent Systems and Computing; Vol. 287).

Research output: Chapter in Book/Report/Conference proceedingChapter

Abdullah, Z, Mohd, F, Mohd Saman, MY, Mat Deris, M, Herawan, T & Hamdan, AR 2014, Mining critical least association rule from oral cancer dataset. in Advances in Intelligent Systems and Computing. vol. 287, Advances in Intelligent Systems and Computing, vol. 287, Springer Verlag, pp. 529-538, 1st International Conference on Soft Computing and Data Mining, SCDM 2014, Parit Raja, Batu Pahat, 16/6/14. https://doi.org/10.1007/978-3-319-07692-8_50
Abdullah Z, Mohd F, Mohd Saman MY, Mat Deris M, Herawan T, Hamdan AR. Mining critical least association rule from oral cancer dataset. In Advances in Intelligent Systems and Computing. Vol. 287. Springer Verlag. 2014. p. 529-538. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-07692-8_50
Abdullah, Zailani ; Mohd, Fatiha ; Mohd Saman, Md Yazid ; Mat Deris, Mustafa ; Herawan, Tutut ; Hamdan, Abdul Razak. / Mining critical least association rule from oral cancer dataset. Advances in Intelligent Systems and Computing. Vol. 287 Springer Verlag, 2014. pp. 529-538 (Advances in Intelligent Systems and Computing).
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