Abstract
We discuss two interpretations of missing attribute values, lost values and “do not care” conditions. Both interpretations may be used for data mining based on characteristic sets. On the other hand, maximal consistent blocks were originally defined for incomplete data sets with “do not care” conditions, using only lower and upper approximations. We extended definitions of maximal consistent blocks to both interpretations while using probabilistic approximations, a generalization of lower and upper approximations. Our main objective is to compare approximations based on characteristic sets with approximations based on maximal consistent blocks in terms of an error rate.
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Clark, P.G., Gao, C., Grzymala-Busse, J.W., Mroczek, T. (2018). A Comparison of Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_40
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DOI: https://doi.org/10.1007/978-3-319-91476-3_40
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