Aggregation in confidence-based concept discovery for multi-relational data mining

Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, we describe a method for generating and using aggregate predicates in an ILP-based concept discovery system and compare its performance in terms of quality of concept discovery with other multi-relational learning systems using aggregation