Abstract
The discovery of functional dependencies (FDs) in relational databases is an important data-mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the FDs by scanning the entire old and new database repeatedly. Some works consider the incremental discovery of FDs in the presence of a new set of tuples added to an old database. In this work, we present two incremental data mining algorithms, top-down and bottom-up, to discover all FDs when deletion of tuples occurred to the database. Based on the principle of monotonicity of FDs [2], we avoid rescanning of the database and thereby reduce computation time. Feasibility and efficiency of the two proposed algorithms are demonstrated through examples.
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Wang, SL., Tsou, WC., Lin, JH., Hong, TP. (2003). Maintenance of Discovered Functional Dependencies: Incremental Deletion. In: Abraham, A., Franke, K., Köppen, M. (eds) Intelligent Systems Design and Applications. Advances in Soft Computing, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44999-7_55
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DOI: https://doi.org/10.1007/978-3-540-44999-7_55
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