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From data analysis to uncertainty knowledge analysis

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Symbolic and Quantitative Approaches to Uncertainty (ECSQARU 1991)

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

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Authors and Affiliations

  1. University Paris Dauphine/INRIA, Frankreich

    E. Diday

Authors
  1. E. Diday
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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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