Abstract
Knowledge reduction is one of the basic contents in rough set theory and one of the most problem in knowledge acquisition. The main objective of this paper is to introduce a new concept of knowledge reduction based on partition. It is referred to as partition reduction. The partition reduction is to unify the definitions of classical knowledge reductions. Classical knowledge reductions such as absolute attribute reduction, relative reduction, distribution reduction, assignment reduction and maximum distribution reduction are special cases of partition reduction. We can establish new types of knowledge reduction to meet our requirements based on partition reduction.
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Pei, X., Wang, Y. (2005). Knowledge Reduction of Rough Set Based on Partition. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_7
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DOI: https://doi.org/10.1007/11508069_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
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