Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.600443
Title: Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications
Author: Bashon, Yasmina Massoud
Awarding Body: University of Bradford
Current Institution: University of Bradford
Date of Award: 2013
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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.
Supervisor: Neagu, Daniel C.; Ridley, Mick J. Sponsor: Libyan Embassy
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.600443  DOI: Not available
Keywords: Similarity Measures ; Fuzzy Geometrical Similarity Model ; Fuzzy Set-Theoretical Similarity Model ; Fuzzy objects ; Heterogeneous data ; Fuzzy attributes ; Numerical attributes ; Categorical attributes ; Data objects ; Comparison
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