Paper Title
IPARO-Interactive Post-Mining Of Association Rules Using Ontologies

In Data Mining, the usefulness of association rules is strongly narrow by the huge amount of delivered rules. To overcome this drawback, we can make use of several techniques such as redundancy reduction, itemset concise representations, and postprocessing. However, as per statistical information, these methods do not assurance that the extracted rules are interesting for the user. Thus, it is difficult to the decision maker and requires essential help with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose the use of Ontologies to improve the integration of user knowledge in the postprocessing task. Second, we propose the Rule Schema so that the user can express goals and expectations concerning the association rules. And finally, an interactive framework is designed to assist the user throughout the analyzing task. Our new approach is applied over voluminous sets of rules, by integrating domain expert knowledge in the postprocessing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.