Rule mining with prior knowledge-a belief networks approach

Zonglin Zhou, Huan Liu, Stan Z. Li, Chin Seng Chua

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases.

Original languageEnglish (US)
Pages (from-to)95-110
Number of pages16
JournalIntelligent Data Analysis
Volume5
Issue number2
DOIs
StatePublished - 2001
Externally publishedYes

Keywords

  • belief networks
  • classification rule
  • correlation rule
  • machine learning
  • rule mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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