Confidence-based concept discovery in relational databases

Date

2009-11-16

Author

Kavurucu, Yusuf
Karagöz, Pınar
Toroslu, İsmail Hakkı

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

180
views

0
downloads

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

Suggestions

OpenMETU
Core

Confidence-based concept discovery in multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2008-03-21) 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 dis...
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...
An ilp-based concept discovery system for multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Department of Computer Engineering (2009) 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. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Induct...
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...
A new hybrid multi-relational data mining technique Toprak, Seda Dağlar; Toroslu, İ. Hakkı; Department of Computer Engineering (2005) 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 w...

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.