Abstract
In this paper, we propose a classification system to induce an intentional definition of a relation from examples, when background knowledge is stored in a relational database composed of several tables and views. Refinement operators have been defined to integrate in a uniform way different induction tools learning numeric and symbolic constraints. The particularity of our approach is to use integrity constraints over the database (keys and foreign keys) to explore the hypotheses space. Moreover new attributes can be introduced, relying on the aggregation operator “group by”.
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P. Atzeni and V. De Antonellis. Relational Database Theory. Benjamin/Cummings Publ. Comp., Redwood City, California, 1993.
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Ad-vances in Knowledge Discovery and Data Mining. MIT Press, Mento Park, 1996.
S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245–286, 1995.
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. The Journal of Logic Programming, 19 & 20:629–680, May 1994.
S. Muggleton, A. Srinivasan, R. King, and M. Sternberg. Biochemical knowledge discovery using Inductive Logic Programming. In H. Motoda, editor, Proc. of the first Conference on Discovery Science, Berlin, 1998. Springer-Verlag.
J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239–266, 1990.
G. Silverstein and M. Pazzani. Relational cliches: constraining constructive induction during relational learning. In Proceedings of the Sixth International Workshop on Machine Learning, Los Altos, CA, 1989. Kaufmann.
T. Turmeaux and C. Vrain. Learning in constraint databases. In Discovery Science, Second International Conference, volume 1721 of LNAI, pages 196–207, Berlin, december 1999. Springer.
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Moal, F., Turmeaux, T., Vrain, C. (2000). Mining Relational Databases. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_63
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DOI: https://doi.org/10.1007/3-540-45372-5_63
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