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A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

Emad Alsukhni, Ahmed AlEroud, Ahmad A. Saifan
Copyright: © 2019 |Volume: 14 |Issue: 1 |Pages: 20
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781522564485|DOI: 10.4018/IJITWE.2019010106
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MLA

Alsukhni, Emad, et al. "A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies." IJITWE vol.14, no.1 2019: pp.112-131. http://doi.org/10.4018/IJITWE.2019010106

APA

Alsukhni, E., AlEroud, A., & Saifan, A. A. (2019). A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies. International Journal of Information Technology and Web Engineering (IJITWE), 14(1), 112-131. http://doi.org/10.4018/IJITWE.2019010106

Chicago

Alsukhni, Emad, Ahmed AlEroud, and Ahmad A. Saifan. "A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies," International Journal of Information Technology and Web Engineering (IJITWE) 14, no.1: 112-131. http://doi.org/10.4018/IJITWE.2019010106

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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.

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