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Mining interesting patterns in multi-relational data with N-ary relationships

: Spyropoulou, E.; Bie, T. de; Boley, M.


Fürnkranz, J.:
Discovery science. Proceedings : 16th international conference, DS 2013, Singapore, October 6-9, 2013
Berlin: Springer, 2013 (Lecture Notes in Computer Science (LNCS) 8140)
ISBN: 978-3-642-40896-0
ISBN: 3-642-40896-6
ISBN: 978-3-642-40897-7
International Conference on Discovery Science (DS) <16, 2013, Singapore>
Fraunhofer IAIS ()

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.