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Transactions on Computational Science V pp 182–205Cite as

Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts

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Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 5540))

Abstract

We investigate the notion of an association reduct. Association reducts represent data-based functional dependencies between the sets of attributes, where it is preferred that possibly smallest sets determine possibly largest sets. We compare the notions of an association reduct to other types of reducts previously studied within the theory of rough sets. We focus particularly on modeling inexactness of dependencies, which is crucial for many real-life data applications. We also study the optimization problems and algorithms that aim at searching for the most interesting approximate association reducts in data.

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Author information

Authors and Affiliations

  1. Infobright Inc., Canada/Poland, Poland

    Dominik Ślęzak

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  1. Dominik Ślęzak
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Editors and Affiliations

  1. University of Calgary, Department of Computer Science, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  3. Theoretical and Empirical Software Engineering Research Centre (TESERC), University of Calgary, 2500 University Drive, NW, T2N 1N4, Calgary, Alberta, Canada

    Yingxu Wang

  4. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Keith C. C. Chan

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Ślęzak, D. (2009). Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds) Transactions on Computational Science V. Lecture Notes in Computer Science, vol 5540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02097-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-02097-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02096-4

  • Online ISBN: 978-3-642-02097-1

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