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Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications.
Bashon, Yasmina M.
Bashon, Yasmina M.
Publication Date
2014-05-02
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Supervisor
Rights
The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
Department of Computing, School of Computing, Informatics and Media
Awarded
2013
Embargo end date
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Additional title
Abstract
This thesis makes an original contribution to knowledge in the fi eld
of data objects' comparison where the objects are described by attributes
of fuzzy or heterogeneous (numeric and symbolic) data types.
Many real world database systems and applications require information
management components that provide support for managing
such imperfect and heterogeneous data objects. For example,
with new online information made available from various sources, in
semi-structured, structured or unstructured representations, new information
usage and search algorithms must consider where such data
collections may contain objects/records with di fferent types of data:
fuzzy, numerical and categorical for the same attributes.
New approaches of similarity have been presented in this research to
support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity
measures presented in this thesis, to handle the vagueness (fuzzy data
type) within data objects. A framework of new and unif ied similarity
measures for comparing heterogeneous objects described by numerical,
categorical and fuzzy attributes has also been introduced.
Examples are used to illustrate, compare and discuss the applications
and e fficiency of the proposed approaches to heterogeneous data comparison.
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Type
Thesis
Qualification name
PhD