Utilize este identificador para referenciar este registo: http://hdl.handle.net/10451/5659
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degois.publication.firstPage9por
degois.publication.lastPage18por
degois.publication.locationVarsóviapor
degois.publication.titleBiocybernetics and Biomedical Engineeringpor
dc.contributor.authorNicolau, Helena Bacelar-
dc.contributor.authorNicolau, Fernando-
dc.contributor.authorSousa, Áurea-
dc.contributor.authorNicolau, Leonor Bacelar-
dc.date.accessioned2012-03-20T12:05:23Z-
dc.date.available2012-03-20T12:05:23Z-
dc.date.issued2009-
dc.identifier.citationBiocybernetics and Biomedical Engineering 2009;29(2):9–18por
dc.identifier.issn0208-5216-
dc.identifier.urihttp://hdl.handle.net/10451/5659-
dc.identifier.urihttp://www.ibib.waw.pl/bbe/bbefulltext/BBE_29_2_009_FT.pdf-
dc.description.abstractCluster analysis or classification usually concerns a set of exploratory multivariate data analysis methods and techniques for finding a clustering structure on a dataset. That may refer either to groups of statistical data units or to groups of variables. In this work we deal with a generalization of this paradigm concerning clustering of complex data described by three different types of variables, frequently present in a three-way context. We obtain compatible versions of the same affinity coefficient for measuring similarity between statistical data units described by those three types of variables. A global generalized similarity coefficient is analyzed for such kind of mixed data, often arising in data mining or knowledge mining.por
dc.description.sponsorshipThis research was partially supported by FCT/POCTI/ and POCTI/FEDER, in the scope of CEAUL Research Project on Applied Multivariate Data Analysis and Modelling.por
dc.language.isoengpor
dc.publisherPolish Academy of Sciencespor
dc.rightsrestrictedAccesspor
dc.subjectCluster analysispor
dc.subjectDifferent type variablespor
dc.subjectSimilarity coefficientpor
dc.subjectThree-way datapor
dc.subjectStatisticspor
dc.titleMeasuring similarity of complex and heterogeneous data in clustering of large data setspor
dc.typearticlepor
dc.peerreviewedyespor
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