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dc.contributor.authorBashon, Yasmina M.
dc.contributor.authorNeagu, Daniel
dc.contributor.authorRidley, Mick J.
dc.date.accessioned2016-10-07T14:29:46Z
dc.date.available2016-10-07T14:29:46Z
dc.date.issued2013-09
dc.identifier.citationBashon 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.
dc.identifier.urihttp://hdl.handle.net/10454/9625
dc.descriptionNo
dc.description.abstractReal-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.
dc.relation.isreferencedbyhttp://dx.doi.org/10.1007/s00500-012-0974-6
dc.subjectSimilarity measures
dc.subject; Fuzzy objects
dc.subject; Fuzzy attributes
dc.subject; Numerical attributes
dc.subject; Categorical attributes
dc.subject; Clustering-algorithm
dc.subject; Classification
dc.subject; Information
dc.subject; Distance
dc.subject; Words
dc.subject; Sets
dc.titleA framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes
dc.status.refereedYes
dc.typeArticle
dc.type.versionNo full-text available in the repository


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