Introduction
Nowadays, we deal with large data tables that include up to billions of objects and up to several thousands of attributes. We often face a question whether we can remove some data from a data table preserving its basic properties, that is – whether a table contains some superfluous data. This chapter provides an introduction to rough set based data preprocessing methods, which are concerned with selection of attributes to reduce the dimensionality and improve the data for subsequent data mining analysis.
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© 2009 Springer-Verlag Berlin Heidelberg
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Stepaniuk, J. (2009). Data Reduction. In: Rough – Granular Computing in Knowledge Discovery and Data Mining. Studies in Computational Intelligence, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70801-8_3
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DOI: https://doi.org/10.1007/978-3-540-70801-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70800-1
Online ISBN: 978-3-540-70801-8
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