Abstract
This paper presents a novel approach to the prediction of null values in relational databases, based on the notion of analogical proportion. We show in particular how an algorithm initially proposed in a classification context can be adapted to this purpose. This work focuses on the case of a transactional database, where attributes are Boolean. The experimental results reported here, even though preliminary, are encouraging since the approach yields a better precision, on average, than the classical nearest neighbors technique.
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Correa Beltran, W., Jaudoin, H., Pivert, O. (2014). Estimating Null Values in Relational Databases Using Analogical Proportions. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_12
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DOI: https://doi.org/10.1007/978-3-319-08852-5_12
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