Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules

Author:

Sakai Hiroshi,Nakata Michinori,Ślęzak Dominik,Watada Junzo

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the VLDB 1994, Morgan Kaufmann, pp. 487–499 (1994)

2. Berti-Equille, L., Harmouch, H., Naumann, F., Novelli, N., Thirumuruganathan, S.: Discovery of genuine functional dependencies from relational data with missing values. Proc. VLDB 2018, 880–892 (2018)

3. Breve, B., Caruccio, L., Deufemia, V., Polese, G.: RENUVER: a missing value imputation algorithm based on relaxed functional dependencies. Proc. EDBT 2022, 52–64 (2022)

4. Frank, A., Asuncion, A.: UCI machine learning repository, Irvine, CA: University of California, School of Information and Computer Science. http://mlearn.ics.uci.edu/MLRepository.html. Accessed 7 Jan 2022

5. Functional dependency, Wikipedia. https://en.wikipedia.org/wiki/Functional_dependency. Accessed 3 Apr 2022

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