Visualization to assist users in association rule mining tasks

Since the definition of the association rule mining problem in Agrawal’s 1993 paper, several efficient algorithms have been introduced to tackle the problem. Nevertheless, most of the approaches focuses on increasing algorithm efficiency, neglecting the fact that association rule mining poses many practical difficulties to users. Mining useful information is not straightforward, and neither is interpreting the set of mined rules or establishing their actual relevance to the knowledge extraction problem at hand. In the last five to six years many approaches to mine association rules based on statistics, pruning, visual techniques and a combination of these have been introduced in the literature. But no general approach in which the user can understand and control the process by interacting with the analytical association rule mining algorithm along its execution has been introduced. In this work, we propose a dynamical framework for association rule mining that integrates interactive visualization techniques in order to allow users to drive the association rule finding process, giving them control and visual cues to ease understanding of both the process and its results.