Mining patterns of dyspepsia symptoms across time points using constraint association rules

Annie Lau, Siew Siew Ong, Ashesh Mahidadia, Achim Hoffmann, Johanna Westbrook, Tatjana Zrimec

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsK.Y. Whang, J. Jeon, K. Shim, J. Srivatava
Place of PublicationBerlin ; London
PublisherSpringer, Springer Nature
Pages124-135
Number of pages12
Volume2637
ISBN (Electronic)3540047603, 9783540047605
Publication statusPublished - 2003
Externally publishedYes
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: 30 Apr 20032 May 2003

Other

Other7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
CountryKorea, Republic of
CitySeoul
Period30/04/032/05/03

Keywords

  • Association rule with constraints
  • Domain knowledge
  • Human interaction
  • Medical knowledge discovery

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  • Cite this

    Lau, A., Ong, S. S., Mahidadia, A., Hoffmann, A., Westbrook, J., & Zrimec, T. (2003). Mining patterns of dyspepsia symptoms across time points using constraint association rules. In K. Y. Whang, J. Jeon, K. Shim, & J. Srivatava (Eds.), Advances in Knowledge Discovery and Data Mining (Vol. 2637, pp. 124-135). Berlin ; London: Springer, Springer Nature.