Supporting Visual Exploration of Discovered Association Rules through Multi-Dimensional Scaling

Association rules are typically evaluated in terms of support and confidence measures, which ensure that discovered rules have enough positive evidence. However, in real-world applications, even considering only those rules with high confidence and support it is not true that all of them are interesting. It may happen that the presentation of all discovered rules can discourage users from interpreting them in order to find nuggets of knowledge. Association rules interpretation can benefit from discovering group of "similar" rules, where (dis)similarity is estimated on the basis of syntactic or semantic characteristics. In this paper, we resort to the multi-dimensional scaling to support a visual exploration of association rules by means of bi-dimensional scatter-plots. An application in the domain of biomedical literature is reported. Results show that the use of this visualization technique is beneficial.