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dc.contributor.authorBillard, Lynne
dc.contributor.authorDiday, Edwin
dc.date.accessioned2011-08-30T14:21:50Z
dc.date.available2011-08-30T14:21:50Z
dc.date.issued2004
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6895
dc.language.isoenen
dc.subjectSymbolic Data Analysisen
dc.subject.ddc519en
dc.titleSymbolic Data Analysis: Definition and Examplesen
dc.typeDocument de travail / Working paper
dc.description.abstractenWith the advent of computers, large, very large datasets have become routine. What is not so routine is how to analyse these data and/or how to glean useful information from within their massive confines. One approach is to summarize large data sets in such a way that the resulting summary dataset is of a manageable size. One consequence of this is that the data may no longer be formatted as single values such as is the case for classical data, but may be represented by lists, intervals, distributions and the like. These summarized data are examples of symbolic data. This paper looks at the concept of symbolic data in general, and then attempts to review the methods currently available to analyse such data. It quickly becomes clear that the range of methodologies available draws analogies with developments prior to 1900 which formed a foundation for the inferential statistics of the 1900’s, methods that are largely limited to small (by comparison) data sets and limited to classical data formats. The scarcity of available methodologies for symbolic data also becomes clear and so draws attention to an enormous need for the development of a vast catalogue (so to speak) of new symbolic methodologies along with rigorous mathematical foundational work for these methods.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages62en
dc.relation.ispartofseriestitleCahiers du Ceremadeen
dc.relation.ispartofseriesnumber61en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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