Abstract
In this paper, we prove that using rough degree of rough set in classic rough sets to measure of uncertainty of knowledge is not comprehensive. Then we define a new measure named rough entropy of rough set, and we prove it is a more comprehensive measure of incompleteness of knowledge about rough set X. At the same time, the research suggests the rough degree of rough set X about knowledge R, the rough entropy of knowledge R, and the rough entropy of rough set X about knowledge R in classic rough sets decrease monotonously as the granularity of information become smaller through finer partitions. These results will be very help for understanding the essence of concept approximation and measure of incompleteness in rough sets.
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© 2004 Springer-Verlag Berlin Heidelberg
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Li, Q., Li, J., Li, X., Li, S. (2004). Evaluation Incompleteness of Knowledge in Data Mining. In: Chi, CH., Lam, KY. (eds) Content Computing. AWCC 2004. Lecture Notes in Computer Science, vol 3309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30483-8_33
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DOI: https://doi.org/10.1007/978-3-540-30483-8_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23898-0
Online ISBN: 978-3-540-30483-8
eBook Packages: Springer Book Archive