Abstract
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.
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Spyropoulou, E., De Bie, T., Boley, M. (2013). Mining Interesting Patterns in Multi-relational Data with N-ary Relationships. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_15
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DOI: https://doi.org/10.1007/978-3-642-40897-7_15
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