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Mining Inconsistent Data with Probabilistic Approximations

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Issues and Challenges in Artificial Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 559))

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Abstract

Generalized probabilistic approximations, defined using both rough set theory and probability theory, are studied using an approximation space (U, R), where R is an arbitrary binary relation. Generalized probabilistic approximations are applicable in mining inconsistent data (data with conflicting cases) and data with missing attribute values.

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Correspondence to J. W. Grzymala-Busse .

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Clark, P.G., Grzymala-Busse, J.W., Hippe, Z.S. (2014). Mining Inconsistent Data with Probabilistic Approximations. In: S. Hippe, Z., L. Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-06883-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-06883-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06882-4

  • Online ISBN: 978-3-319-06883-1

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