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2014 | OriginalPaper | Buchkapitel

Mining Inconsistent Data with Probabilistic Approximations

verfasst von : P. G. Clark, J. W. Grzymala-Busse, Z. S. Hippe

Erschienen in: Issues and Challenges in Artificial Intelligence

Verlag: Springer International Publishing

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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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Literatur
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Metadaten
Titel
Mining Inconsistent Data with Probabilistic Approximations
verfasst von
P. G. Clark
J. W. Grzymala-Busse
Z. S. Hippe
Copyright-Jahr
2014
DOI
https://doi.org/10.1007/978-3-319-06883-1_8

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