Mining context-aware association rules using grammar-based genetic programming

J.M. Luna, M. Pechenizkiy, M.J. del Jesus, S. Ventura

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)


Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

Original languageEnglish
Article number8049471
Pages (from-to)3030-3044
Number of pages15
JournalIEEE Transactions on Cybernetics
Issue number11
Publication statusPublished - 1 Nov 2018


  • Association rules
  • context awareness
  • contextual features


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