Post-Processing of Discovered Association Rules Using Ontologies
Machine Learning
2009-10-05 v1
Abstract
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task. On the one hand, we represent user domain knowledge using a Domain Ontology over database. On the other hand, a novel technique is suggested to prune and to filter discovered rules. The proposed framework was applied successfully over the client database provided by Nantes Habitat.
Cite
@article{arxiv.0910.0349,
title = {Post-Processing of Discovered Association Rules Using Ontologies},
author = {Claudia Marinica and Fabrice Guillet and Henri Briand},
journal= {arXiv preprint arXiv:0910.0349},
year = {2009}
}