Finding relational redescriptions

Esther Galbrun (1, 2) , Angelika Kimmig (3)

1 Helsingin yliopisto = Helsingfors universitet = University of Helsinki
2 HIIT - Helsinki Institute for Information Technology
3 Department of Computer Science - K.U.Leuven

We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline Inductive Logic Programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks.

Mots clés: Relational data mining; Redescription mining; Relational query mining; Inductive Logic Programming; Graph mining

Domaines: Intelligence artificielle [cs.AI]

Esther Galbrun, Angelika Kimmig. Finding relational redescriptions. Machine Learning, 2014, 96 (3), pp.225-248. ⟨10.1007/s10994-013-5402-3⟩. ⟨hal-01399196⟩