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On Approximation Algorithms for Data Mining Applications

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Efficient Approximation and Online Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3484))

Abstract

We aim to present current trends in the theoretical computer science research on topics which have applications in data mining. We briefly describe data mining tasks in various application contexts. We give an overview of some of the questions and algorithmic issues that are of concern when mining huge amounts of data that do not fit in main memory.

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  1. National Technical University of Athens, Greece

    Foto N. Afrati

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  1. Foto N. Afrati
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  1. IBISC, CNRS FRE 3190, Université d’Evry Val d’Essonne, Boulevard François Mitterand, 91025, Evry Cedex, France

    Evripidis Bampis

  2. Institute for Computer Science, University of Kiel, Olshausenstrasse 40, 24118, Kiel, Germany

    Klaus Jansen

  3. Brown University, 02918, Providence, RI, USA

    Claire Kenyon

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Afrati, F.N. (2006). On Approximation Algorithms for Data Mining Applications. In: Bampis, E., Jansen, K., Kenyon, C. (eds) Efficient Approximation and Online Algorithms. Lecture Notes in Computer Science, vol 3484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671541_1

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  • DOI: https://doi.org/10.1007/11671541_1

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