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
A new hybrid multi-relational data mining technique
Download
index.pdf
Date
2005
Author
Toprak, Seda Dağlar
Metadata
Show full item record
Item Usage Stats
206
views
76
downloads
Cite This
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 work, we propose a relational concept learning technique, which adopts concept descriptions as associations between the concept and the preconditions to this concept and employs a relational upgrade of association rule mining search heuristic, APRIORI rule, to effectively prune the search space. The proposed system is a hybrid predictive inductive logic system, which utilizes inverse resolution for generalization of concept instances in the presence of background knowledge and refines these general patterns into frequent and strong concept definitions with a modified APRIORI-based specialization operator. Two versions of the system are tested for three real-world learning problems: learning a linearly recursive relation, predicting carcinogenicity of molecules within Predictive Toxicology Evaluation (PTE) challenge and mesh design. Results of the experiments show that the proposed hybrid method is competitive with state-of-the-art systems.
Subject Keywords
Databases.
URI
http://etd.lib.metu.edu.tr/upload/12606150/index.pdf
https://hdl.handle.net/11511/15140
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Using fuzzy Petri nets for static analysis of rule-bases
Bostan-Korpeoglu, B; Yazıcı, Adnan (2004-01-01)
We use a Fuzzy Petri Net (FPN) structure to represent knowledge and model the behavior in our intelligent object-oriented database environment, which integrates fuzzy, active and deductive rules with database objects. However, the behavior of a system can be unpredictable due to the rules triggering or untriggering each other (non-termination). Intermediate and final database states may also differ according to the order of rule executions (non-confluence). In order to foresee and solve problematic behavior...
An attempt to classify Turkish district data : K-Means and Self-Organizing Map (SOM) algorithms
Aksoy, Ece; Işık, Oğuz; Department of Geodetic and Geographical Information Technologies (2004)
There is no universally applicable clustering technique in discovering the variety of structures display in data sets. Also, a single algorithm or approach is not adequate to solve every clustering problem. There are many methods available, the criteria used differ and hence different classifications may be obtained for the same data. While larger and larger amounts of data are collected and stored in databases, there is increasing the need for efficient and effective analysis methods. Grouping or classific...
Migration of Data from Relational Database to Graph Database
Unal, Yelda; Oğuztüzün, Mehmet Halit S. (2018-01-01)
Relational databases have been widely used in many applications until today and they have met needs for data-intensive domains and transactions, but today data is growing faster than ever and extracting information from this huge data is becoming more challenging. Growing size of data and number of connections between data items reduces performance because relational databases use many complex join operations to query and access data. As a solution, graph database store these connections between entities an...
New transitive closure algorithm for recursive query processing in deductive databases
Toroslu, İsmail Hakkı (1992-01-01)
© 1992 IEEE.The development of effic1.e11t algorithms to process the different forms of the transitive-closure (TC) queries within the context of large database systems has recently attracted a large amom1t of research efforts. In this paper, we present a neic algorithm suitable for full transitive closure problem, which zs used to solve uninstentiated recursive qi1enes in deductive databases. In this new algorithm there are two phases. In the first phase a general graph is condensed into an acyclic graph a...
An efficient transitive closure algorithm for distributed databases
Toroslu, İsmail Hakkı (1993-01-01)
© 1993 IEEE.Because most of the recognizable queries in deductive databases can be transformed into transitive-closure (TC) problem, the development of efficient algorithms to process the different forms of TC problems within the context of large database systems has recently attracted a large amount of research efforts. However, the parallelization of the computation of TC is still a new issue and there are only a few and recent parallel TC algorithms. Most of these parallel algorithms mere developed for s...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. D. Toprak, “A new hybrid multi-relational data mining technique,” M.S. - Master of Science, Middle East Technical University, 2005.