A user-driven and quality-oriented visualization for mining association rules

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge validation is one of the most problematic steps in an association rule discovery process. In order to find relevant knowledge for decision-making, the user needs to really rummage through the rules. Visualization can be very beneficial to support him/her in this task by improving the intelligibility of the large rule sets and enabling the user to navigate inside them. We propose to answer the association rule validation problem by designing a human-centered visualization method for the rule rummaging task. This new approach based on a specific rummaging model relies on rule interestingness measures and on interactive rule subset focusing and mining. We have implemented our representation by developing a first experimental prototype called ARVis.