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dc.contributor.authorAnderson, Granten_NZ
dc.date.accessioned2009-05-20T22:42:23Z
dc.date.available2009-06-23T22:42:23Z
dc.date.issued2008en_NZ
dc.identifier.citationAnderson, G. (2008). Random Relational Rules (Thesis). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2562en
dc.identifier.urihttps://hdl.handle.net/10289/2562
dc.description.abstractIn the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques. This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests. The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient. Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectrelational data miningen_NZ
dc.subjectrandom relational rulesen_NZ
dc.subjectpropositionalisationen_NZ
dc.titleRandom Relational Rulesen_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineSchool of Computing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelDoctoral
uow.date.accession2009-05-20T22:42:23Zen_NZ
uow.date.available2009-06-23T22:42:23Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20090520.112447en_NZ
uow.date.migrated2009-07-07T22:42:23Zen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ


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