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Finding relational redescriptions

Galbrun, Esther and Kimmig, Angelika ORCID: 2014. Finding relational redescriptions. Machine Learning 96 (3) , pp. 225-248. 10.1007/s10994-013-5402-3

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag (Germany)
ISSN: 0885-6125
Date of First Compliant Deposit: 20 November 2017
Date of Acceptance: 24 July 2013
Last Modified: 12 Nov 2023 08:12

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