2007 IEEE International Conference on Granular Computing (GRC 2007) A Comparison of Three Approximation Strategies for Incomplete Data Sets San Jose, California November 02-November 04 ISBN: 0-7695-3032-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/GrC.2007.119
In this paper we consider incomplete data sets, i.e., data sets with missing attribute values. Two different types of missing attribute values are studied: lost and "do not care". Furthermore, three definitions of approximations are dis- cussed: singleton, subset, and concept. Theoretically, sin- gleton approximations should not be used in data mining since concepts approximated by singleton approximations are not definable. However, we conducted a number of experiments on 44 different incomplete data sets using all three approximation definitions and our results show that none of these approximations is superior to the other.
Citation:
Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse, Zdzislaw S. Hippe, Wojciech Rzasa, "A Comparison of Three Approximation Strategies for Incomplete Data Sets," grc, pp.301, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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