A hybrid pre-post constraint-based framework for discovering multi-dimensional association rules using ontologies

Emad Alsukhni, Ahmed AlEroud, Ahmad A. Saifan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.

Original languageEnglish (US)
Pages (from-to)112-131
Number of pages20
JournalInternational Journal of Information Technology and Web Engineering
Volume14
Issue number1
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Keywords

  • Association rules
  • Data mining
  • Domain ontology
  • Rule pruning

ASJC Scopus subject areas

  • Computer Science(all)

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