Confidence-based concept discovery in relational databases

Date

2009-11-16

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, we improve an ILP-based concept discovery method, namely confidence-based concept discovery (C 2 D) by removing the dependence on order of target instances in the relational database. In this method, the generalization step of the basic algorithm of C 2 D is modified so that all possible frequent rules in a priori lattice can be searched in an efficient manner. Moreover, this improved version directly finds transitive rules in the search space. A set of experiments is conducted to compare the performance of proposed method with the basic version in terms of support and confidence.

Subject Keywords

Relational databases, Data mining, Learning systems, Logic programming, Lattices, Computer science, Data engineering, Association rules, Absorption, Data structures

URI

https://hdl.handle.net/11511/52586

DOI

https://doi.org/10.1109/csie.2009.267

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. Kavurucu, P. Karagöz, and İ. H. Toroslu, “Confidence-based concept discovery in relational databases,” 2009, vol. 4, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52586.