Meta-association rules for fusing regular association rules from different databases

Association rules have been widely used in many application areas to extract from raw data new and useful information expressed in a comprehensive way for decision makers. Nowadays, with the increase of the volume and the variety of data, the classical data mining workflow is showing insufficient. We can expect in the near future that, more often than not, several mining processes will be carried out over the same or different sources, thus requiring extracted information to be fused in order to provide a unified, not overwhelming view to the user. In this paper we propose a new technique for fusing associations rules. The notion of meta-association rule is introduced for that purpose. Meta-association rules are association rules where the antecedent or the consequent can contain regular rules that have been previously extracted with a high reliability in a high percentage of the source databases. We illustrate out proposal with an example in the domain of crime data analysis.