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European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 1991: Symbolic and Quantitative Approaches to Uncertainty pp 153–160Cite as

From data analysis to uncertainty knowledge analysis

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 548))

Abstract

The main aim of the symbolic approach in statistics is to extend problems, methods and algorithms used on classical data to more complex data called “symbolic objects” which are well adapted to representing knowledge and which “unify” unlike usual observations which characterize “individual things”. We introduce two kinds of symbolic objects: boolean and possibilist. We briefly present some of their qualities and properties. We give some ideas on how statistics and data analysis may be extended on these objects. Finally four kinds of data analysis problems are presented.

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References

  1. Brito P., Diday E., (1990), “Pyramidal representation of symbolic objects”, in NATO ASI Series, Vol. F 61 Knowledge Data and computer-assisted Decisions edited by Schader and W. Gaul. Springer Verlag.

    Google Scholar 

  2. Dempster A.P., (1990), “Construction and local computation aspects of network belief functions, in Influence Diagram, Belief Nets, and Decision Analysis”, Wiley, New York, Chap. 6.

    Google Scholar 

  3. Diday E., (1990), “Knowledge representation and symbolic data analysis”, in NATO ASI Series, Vol. F 61 Knowledge Data and computer-assisted Decisions edited by Schader and W. Gaul. Springer Verlag.

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  4. Diday E., (1991), “Objets modaux pour l'analyse des connaissances”, in “Induction symbolique numérique à partir des données” CEPADUES.

    Google Scholar 

  5. Dubois D., Prade H., (1988), “Possibility theory”, Plenum New York.

    Google Scholar 

  6. Lebbe J., Vignes R., Darmoni S., (1990), “Symbolic numeric approach for biological knowledge representation: a medical example with creation of identification graphs”, in: Proc. of Conf. on Data Analysis, Learning Symbolic and Numerical Knowledge, Antibes ed. E. Diday, Nova Science Publishers, Inc., New York.

    Google Scholar 

  7. Pearl J., (1990), “Reasoning with belief functions: an analysis of compatibility”, Int. Journal of approximate reasoning, Vol. 4, N. 5/6, pp. 363.

    Google Scholar 

  8. Zadeh L.A.(1971), “Quantitative fuzzy semantics”, Information Sciences, 159–176.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University Paris Dauphine/INRIA, Frankreich

    E. Diday

Authors
  1. E. Diday
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Editor information

Rudolf Kruse Pierre Siegel

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© 1991 Springer-Verlag Berlin Heidelberg

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Diday, E. (1991). From data analysis to uncertainty knowledge analysis. In: Kruse, R., Siegel, P. (eds) Symbolic and Quantitative Approaches to Uncertainty. ECSQARU 1991. Lecture Notes in Computer Science, vol 548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54659-6_82

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  • DOI: https://doi.org/10.1007/3-540-54659-6_82

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54659-7

  • Online ISBN: 978-3-540-46426-6

  • eBook Packages: Springer Book Archive

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