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2014 | OriginalPaper | Chapter

Mining Inconsistent Data with Probabilistic Approximations

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

Published in: Issues and Challenges in Artificial Intelligence

Publisher: 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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previous chapter A Comparison of Two Approaches Used for Intelligent Planning of Complex Chemical Syntheses
next chapter Computer-Aided Relative Assessment of Complex Effects of Decisions
Literature
Clark PG, Grzymala-Busse JW (2011) Experiments on probabilistic approximations. In: Proceedings of IEEE international conference on granular computing, pp 144–149
Clark PG, Grzymala-Busse JW (2012) Experiments using three probabilistic approximations for rule induction from incomplete data sets. In: Proceedings of European conference on data mining, pp 72–78
Clark PG, Grzymala-Busse JW, Kuehnhausen M (2012) Local probabilistic approximations for incomplete data. In: Proceedings of 20th international symposium on methodologies for intelligent systems, pp 93–98
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Metadata
Title
Mining Inconsistent Data with Probabilistic Approximations
Authors
P. G. Clark
J. W. Grzymala-Busse
Z. S. Hippe
Copyright Year
2014
Book
Issues and Challenges in Artificial Intelligence

Print ISBN: 978-3-319-06882-4

Electronic ISBN: 978-3-319-06883-1

Copyright Year: 2014

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

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