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    A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes

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    Publication date
    2013-09
    Author
    Bashon, Yasmina M.
    Neagu, Daniel
    Ridley, Mick J.
    Keyword
    Similarity measures
    ; Fuzzy objects
    ; Fuzzy attributes
    ; Numerical attributes
    ; Categorical attributes
    ; Clustering-algorithm
    ; Classification
    ; Information
    ; Distance
    ; Words
    ; Sets
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    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex data. In this paper, a framework of new and unified similarity measures is proposed for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes. Examples are used to illustrate, compare and discuss the applications and efficiency of the proposed approach to heterogeneous data comparison and clustering.
    URI
    http://hdl.handle.net/10454/9625
    Version
    No full-text available in the repository
    Citation
    Bashon Y, Neagu D and Ridley MJ (2013) A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes. Soft Computing. 17(9): 1595-1615.
    Link to publisher’s version
    http://dx.doi.org/10.1007/s00500-012-0974-6
    Type
    Article
    Collections
    Engineering and Informatics Publications

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