The complex structure of relational data makes the process of knowledge discovery from data a more challenging task compared with the single table data structure. The usefulness of granular computing based approaches to mining data stored in a single table is a driving force for adapting this method to relational data. This paper proposes relation-based granules that are defined in a granular computing based approach to mining relational data. The relations are used to represent relational data and patterns to be discovered. Thanks to this representation, the generation of patterns can be speeded up. The representation also makes it possible to discover richer knowledge from relational data. 
Relation-based granules to represent relational data and patterns