A Performance based Multi-relational Data Mining Technique

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Santosh Shakya
Gopal Patidar


Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the
tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes
intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics
and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning
technique, which adopts concept descriptions as associations between the concept and the preconditions to this concept and employs a relational
upgrade of association rule mining search heuristic, APRIORI rule, to effectively prune the search space? The proposed system is a hybrid
predictive inductive logic system, which utilizes inverse resolution for generalization of concept instances in the presence of background
knowledge and refines these general patterns into frequent and strong concept definitions with a modified APRIORI-based specialization
operator. Two versions of the system are tested for three real-world learning problems: learning a linearly recursive relation, predicting
carcinogenicity of molecules within Predictive Toxicology Evaluation (PTE) challenge and mesh design. Results of the experiments show that
the proposed hybrid method is competitive with state-of-the-art systems.

Keywords: Multi-Relational Learning, ILP, Association Rule-Mining, APRIORI.


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