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hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorDiday, Edwin
dc.date.accessioned2018-05-23T09:49:41Z
dc.date.available2018-05-23T09:49:41Z
dc.date.issued2016
dc.identifier.issn1939-5108
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17761
dc.description.abstractfrPenser en terme de classes en Science des donnéesen
dc.language.isoenen
dc.subjectdata scienceen
dc.subjectdata miningen
dc.subjectclassificationen
dc.subjectlearningen
dc.subjectsymbolic data analysisen
dc.subjectfunctional analysisen
dc.subjectBayesianen
dc.subjectmultilevel analysisen
dc.subjectcomplex dataen
dc.subjectbig dataen
dc.subjectgranular computingen
dc.subjectcompositional dataen
dc.subjectScience des donnéesen
dc.subjectApprentissage Automatique à base de corpusen
dc.subject.ddc519en
dc.titleThinking by classes in Data Science: the symbolic data analysis paradigmen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenData Science, considered as a science by itself, is in general terms, the extraction of knowledge from data. Symbolic data analysis (SDA) gives a new way of thinking in Data Science by extending the standard input to a set of classes of individual entities. Hence, classes of a given population are considered to be units of a higher level population to be studied. Such classes often represent the real units of interest. In order to take variability between the members of each class into account, classes are described by intervals, distributions, set of categories or numbers sometimes weighted and the like. In that way, we obtain new kinds of data, called ‘symbolic’ as they cannot be reduced to numbers without losing much information. The first step in SDA is to build the symbolic data table where the rows are classes and the variables can take symbolic values. The second step is to study and extract new knowledge from these new kinds of data by at least an extension of Computer Statistics and Data Mining to symbolic data. SDA is a new paradigm which opens up a vast domain of research and applications by giving complementary results to classical methods applied to standard data. SDA also gives answers to big data and complex data challenges as big data can be reduced and summarized by classes and as complex data with multiple unstructured data tables and unpaired variables can be transformed into a structured data table with paired symbolic‐valued variables.en
dc.relation.isversionofjnlnameWiley Interdisciplinary Reviews. Computational Statistics
dc.relation.isversionofjnlvol8en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2016-08
dc.relation.isversionofjnlpages172–205en
dc.relation.isversionofdoi10.1002/wics.1384en
dc.relation.isversionofjnlpublisherWileyen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2018-05-23T09:45:59Z
hal.author.functionaut


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