Aggregation in confidence-based concept discovery for multi-relational data mining

Date

2008-12-01

Author

Kavurucu, Yusuf
Senkul, Pinar
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 describe a method for generating and using aggregate predicates in an ILP-based concept discovery system and compare its performance in terms of quality of concept discovery with other multi-relational learning systems using aggregation.

Subject Keywords

Aggregate predicates, Data mining, ILP, MRDM

URI

https://hdl.handle.net/11511/87036
http://www.iadisportal.org/digital-library/aggregation-in-confidence-based-concept-discovery-for-multi-relational-data-mining

Conference Name

MCCSIS 2008 : IADIS Multi Conference on Computer Science and Information Systems

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. Kavurucu, P. Senkul, and İ. H. Toroslu, “Aggregation in confidence-based concept discovery for multi-relational data mining,” 2008, p. 43, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87036.