Abstract
Over the past decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explo- sion has pointed out the need of techniques or algorithms in order to extract and discover non-trivial, unknown and potentially useful infor- mation from large data sets. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases. The extracted knowledge can be used to answer cooperative queries, and facilitate semantic query optimization. Relational databases create new type of problems for knowledge discovery such as missing values for some attributes and a key issue in any discovery system is to ensure the completeness of the discovered knowledge.
In this paper, we address the problem of missing values in relational databases. We present an approach to complete or augment a classical relation containing missing values. This is done by exploiting the useful information yielded by the discovered knowledge represented by formal concepts.
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Yahia, S.B., Arour, K., Jaoua, A. (2000). Completing missing values using discovered formal concepts. In: Ibrahim, M., Küng, J., Revell, N. (eds) Database and Expert Systems Applications. DEXA 2000. Lecture Notes in Computer Science, vol 1873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44469-6_69
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DOI: https://doi.org/10.1007/3-540-44469-6_69
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