Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
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
232
views
0
downloads
Cite This
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
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
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.