Confidence-based concept discovery in multi-relational data mining

Date

2008-03-21

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, a new ILP-based concept discovery method is described in which 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. A set of experiments are conducted to test the performance of the new method.

Subject Keywords

Multi-relational data mining, Concept discovery, ILP

URI

https://hdl.handle.net/11511/55512

Conference Name

International Multiconference of Engineers and Computer Scientists

Collections

Department of Computer Engineering, Conference / Seminar

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Improving the scalability of ILP-based multi-relational concept discovery system through parallelization Mutlu, Ayşe Ceyda; Karagöz, Pınar; Kavurucu, Yusuf (2012-03-01) Due to the increase in the amount of relational data that is being collected and the limitations of propositional problem definition in relational domains, multi-relational data mining has arisen to be able to extract patterns from relational data. In order to cope with intractably large search space and still to be able to generate high-quality patterns. ILP-based multi-relational data mining and concept discovery systems employ several search strategies and pattern limitations. Another direction to cope w...
Confidence-based concept discovery in relational databases Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2009-11-16) 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 con...
Aggregation in confidence-based concept discovery for multi-relational data mining Kavurucu, Yusuf; Senkul, Pinar; Toroslu, İsmail Hakkı (null; 2008-12-01) 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 ge...
ILP-based concept discovery in multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (Elsevier BV, 2009-11-01) 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 discov...
Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (Elsevier BV, 2010-12-01) 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 introduce an ILP-based c...

Citation Formats

Y. Kavurucu, P. Karagöz, and İ. H. Toroslu, “Confidence-based concept discovery in multi-relational data mining,” presented at the International Multiconference of Engineers and Computer Scientists, Hong Kong, PEOPLES R CHINA, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55512.