Mining Interesting Patterns in Multi-relational Data with N-ary Relationships

We present a novel method for mining local patterns from multi-relational data in which relationships can be of any arity. More specifically, we define a new pattern syntax for such data, develop an efficient algorithm for mining it, and define a suitable interestingness measure that is able to take into account prior information of the data miner. Our approach is a strict generalisation of prior work on multi-relational data in which relationships were restricted to be binary, as well as of prior work on local pattern mining from a single n-ary relationship. Remarkably, despite being more general our algorithm is comparably fast or faster than the state-of-the-art in these less general problem settings.