Meta-association rules for mining interesting associations in multiple datasets

Process flow: from original datasets to final meta-association rules. The process starts from a set of databases {D1, …, Dk} which share some of their content, i.e. they have attributes in common. After applying a rule extraction procedure, we obtain k sets of association rules represented by Ri. We are interested in searching associations between the already extracted rules in the sets Ri. For achieving this, we create a meta-database D collecting the information. We propose two different ways, by considering only presence-absence of rules (crisp meta-database, D) or taking into account their reliability represented by a degree in the unit interval (fuzzy meta-database, D˜). We can also introduce additional information into the process by adding new attributes about the original datasets Di. After this the so-called meta-association rules are mined. Examples of meta-association rules are depicted in the right part of the figure relating the primary rules and the added attributes. The paper explains and compares both proposals (crisp and fuzzy), proposes a level-based mining algorithm using the RL-theory for the representation of fuzziness and makes some experimentation with synthetic and real data. 73