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    Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications.

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    PhD_Thesis_Yasmina-Bashon_2013-06-18.pdf (3.178Mb)
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    Publication date
    2014-05-02
    Author
    Bashon, Yasmina M.
    Supervisor
    Neagu, Daniel
    Ridley, Mick J.
    Keyword
    Similarity Measures
    Fuzzy Geometrical Similarity Model
    Fuzzy Set-Theoretical Similarity Model
    Fuzzy objects
    Heterogeneous data
    Fuzzy attributes
    Numerical attributes
    Categorical attributes
    Data objects
    Comparison
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Computing, School of Computing, Informatics and Media
    Awarded
    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.
    URI
    http://hdl.handle.net/10454/6305
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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