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Problems in Constructing an Empirical Theory of Data Mining

  • Conference paper
Knowledge Processing and Data Analysis (KPP 2007, KONT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6581))

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Abstract

The paper describes the structure of empirical theories and analyzes the nature of problems inherent in data mining. A formal description an empirical theory of data mining is given. A general approach to the construction of data mining methods based on the function of rival similarity (FRiS-function) is presented. Application of this function allows the construction of a new class of data mining methods and strengthens empirical theory.

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References

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Author information

Authors and Affiliations

  1. Sobolev Institute of Mathematics of the Siberian Branch, of the Russian Academy of Sciences, pr. Koptyug 4, Novosibirsk, 630090, Russia

    Nikolay G. Zagoruiko

Authors
  1. Nikolay G. Zagoruiko
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Editor information

Editors and Affiliations

  1. Hochschule Darmstadt, Fachbereich Mathematik und Naturwissenschaften, Holzhofallee 38, 64295, Darmstadt, Germany

    Karl Erich Wolff

  2. Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Koptyug prosp. 4, 630090, Novosibirsk, Russia

    Dmitry E. Palchunov

  3. Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Koptyug prosp. 4, 630090, Novosibirsk, Russia

    Nikolay G. Zagoruiko

  4. Fachbereich Informatik, Hochschule Darmstadt, Schoefferstraße 8b, 64295, Darmstadt, Germany

    Urs Andelfinger

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Zagoruiko, N.G. (2011). Problems in Constructing an Empirical Theory of Data Mining. In: Wolff, K.E., Palchunov, D.E., Zagoruiko, N.G., Andelfinger, U. (eds) Knowledge Processing and Data Analysis. KPP KONT 2007 2007. Lecture Notes in Computer Science(), vol 6581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22140-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-22140-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22139-2

  • Online ISBN: 978-3-642-22140-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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