Post-Processing of Discovered Association Rules Using Ontologies

TL;DR

This paper introduces a novel ontology-based framework for post-processing association rules in data mining, enhancing rule filtering and pruning by integrating user domain knowledge through an interactive, iterative approach.

Contribution

It presents a new method that leverages domain ontologies for improved rule filtering and pruning, with an interactive framework to support user analysis.

Findings

Successful application on Nantes Habitat database

Enhanced rule filtering using ontologies

Improved user interaction in rule analysis

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.