ILP-based concept discovery in multi-relational data mining

Date

2009-11-01

Author

Kavurucu, Yusuf
Karagöz, Pınar
Toroslu, İsmail Hakkı

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Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discovery method, namely Confidence-based Concept Discovery (C(2)D), is described in which strong declarative biases and user-defined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. We also describe how to define and use aggregate predicates as background knowledge in the proposed method. In order to use aggregate predicates, we show how to handle numerical attributes by using comparison operators on them. Finally, we analyze the effect of incorporating unrelated facts for generating transitive rules on the proposed method. A set of experiments are conducted on real-world problems to test the performance of the proposed method.

Subject Keywords

General Engineering, Artificial Intelligence, Computer Science Applications

URI

https://hdl.handle.net/11511/34730

Journal

EXPERT SYSTEMS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.eswa.2009.02.100

Collections

Department of Computer Engineering, Article

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Citation Formats

Y. Kavurucu, P. Karagöz, and İ. H. Toroslu, “ILP-based concept discovery in multi-relational data mining,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 11418–11428, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34730.