Research in relational data mining has two major directions: finding global models of a relational database and the discovery of local relational patterns within a database. While relational patterns show how attribute values co-occur in detail, their huge numbers hamper their usage in data analysis. Global models, on the other hand, only provide a summary of how different tables and their attributes relate to each other, lacking detail of what is going on at the local level.

Frequent item set mining is a major data mining research area. Generalising from the standard single table case to a multi-relational setting is simple in principle, but hard in practice. That is, it is simple to define frequent item sets in the multi-relational setting, as well as extending the A-Priori algorithm. It is hard, because the well-known frequent pattern explosion at low min-sup settings is far worse than it is in the standard case. In this paper we introduce an effective algorithm for the discovery of frequent, multi-relational item sets. These relational patterns show which item sets occur together. Answering questions like: ‘What type of Books are bought together with what Record types?’. Hence, they provide a symmetric insight in the relation and reveal patterns that are relevant with respect to the relation. It extends our earlier work on using MDL to discover a small set of characteristic item sets. The algorithm, R-KRIMP, first discovers the small set of characteristic patterns in the single tables and then combines these to find a small set of characteristic multi-relational item sets. This reduces the original search space dramatically and, hence, brings down the computational complexity by orders of magnitude. In the experiments we show that this approach yields a very good approximation of the naive approach, joining all tables into one huge table, while being far more efficient.