Abstract
In this paper we analyze the basic concepts of rough set theory, lower and upper approximations, defined in an approximation space \((U,\textbf{L})\), where U is a nonempty and finite set and L is a fixed family of subsets of U. Some definitions of such lower and upper approximations are well known, some are presented in this paper for the first time. Our new definitions better accommodate applications to mining incomplete data, i.e., data with missing attribute values. An illustrative example is also presented in this paper.
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Grzymala-Busse, J.W., Rzasa, W. (2009). Local Approximations. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_2
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DOI: https://doi.org/10.1007/978-3-642-04394-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
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